From 734129d9c6610715e17ccfcde050f954b2b9e1bc Mon Sep 17 00:00:00 2001 From: Scarlett Attensil Date: Sat, 16 May 2026 10:37:37 -0700 Subject: [PATCH 1/7] Roll forward to launchdarkly-server-sdk-ai 0.20 Apply SDK 0.20 API surface changes (mirrors PR #24): - Import from ldai instead of ldai.client - LDAIAgentConfig -> AIAgentConfigRequest, LDAIAgentDefaults -> AIAgentConfigDefault - ai_client.agent(config, ctx) -> ai_client.agent_config(key, ctx, default=...) - config.tracker (property) -> config.create_tracker() (factory) - tracker.track_latency_ms() -> tracker.track_duration() - Bump launchdarkly-server-sdk-ai to >=0.20.0 Also drop stale ld-aic-cicd entry from uv.lock dev deps (not in pyproject). --- agents/ld_agent_helpers.py | 5 +- agents/security_agent.py | 13 +- agents/supervisor_agent.py | 15 +- api/main.py | 2 +- api/services/agent_service.py | 5 +- config_manager.py | 29 +- pyproject.toml | 2 +- utils/metrics.py | 16 +- uv.lock | 689 +--------------------------------- 9 files changed, 58 insertions(+), 718 deletions(-) diff --git a/agents/ld_agent_helpers.py b/agents/ld_agent_helpers.py index 1489107..cf6d939 100644 --- a/agents/ld_agent_helpers.py +++ b/agents/ld_agent_helpers.py @@ -275,7 +275,7 @@ async def create_agent_with_fresh_config( prompt=agent_config.instructions ) - return agent, agent_config.tracker, False, effective_recursion_limit + return agent, agent_config.create_tracker(), False, effective_recursion_limit except Exception as e: log_student(f"ERROR in create_agent_with_fresh_config: {e}") @@ -355,8 +355,7 @@ async def ainvoke(self, request_data: dict) -> dict: # Track success and latency tracker.track_success() latency_ms = int((time.time() - start_time) * 1000) - if hasattr(tracker, 'track_latency_ms'): - tracker.track_latency_ms(latency_ms) + tracker.track_duration(latency_ms) # Extract token usage from new messages new_messages = response['messages'][prev_message_count:] diff --git a/agents/security_agent.py b/agents/security_agent.py index bf55c71..0579069 100644 --- a/agents/security_agent.py +++ b/agents/security_agent.py @@ -112,6 +112,9 @@ async def call_model(state: AgentState): "response": "Security check disabled" } + # Create tracker for token, cost, and success/error metrics + tracker = agent_config.create_tracker() + # Create model with structured output and up-to-date instructions from agents.ld_agent_helpers import map_provider_to_langchain, create_bedrock_chat_model from utils.bedrock_helpers import normalize_bedrock_provider @@ -212,7 +215,7 @@ def _select_provider_and_model(default_provider: str, default_model: str) -> tup output=usage_data.get("output_tokens", 0), total=usage_data.get("total_tokens", 0) ) - agent_config.tracker.track_tokens(token_usage) + tracker.track_tokens(token_usage) log_student(f"SECURITY PII DETECTION TOKENS: {token_usage.total} tokens ({token_usage.input} in, {token_usage.output} out)") # Track cost metric with AI Config metadata for experiment attribution @@ -229,7 +232,7 @@ def _select_provider_and_model(default_provider: str, default_model: str) -> tup log_student(f"COST TRACKING: ${cost:.6f} for {agent_config.model.name}") # Track success metric - agent_config.tracker.track_success() + tracker.track_success() # Extract structured results detected = pii_result.detected @@ -248,11 +251,11 @@ def _select_provider_and_model(default_provider: str, default_model: str) -> tup } except Exception: - + # Track error with LDAI metrics try: - if 'agent_config' in locals() and agent_config and hasattr(agent_config, 'tracker'): - agent_config.tracker.track_error() + if 'agent_config' in locals() and agent_config: + agent_config.create_tracker().track_error() except Exception: pass diff --git a/agents/supervisor_agent.py b/agents/supervisor_agent.py index 11894d6..cd71bec 100644 --- a/agents/supervisor_agent.py +++ b/agents/supervisor_agent.py @@ -119,11 +119,14 @@ def create_supervisor_agent(supervisor_config, support_config, security_config, # Import needed modules for model creation from langchain.chat_models import init_chat_model - + # Create child agents with config manager support_agent = create_support_agent(support_config, config_manager) security_agent = create_security_agent(security_config, config_manager) + # Create tracker for the supervisor's own LLM calls and orchestration metrics + supervisor_tracker = supervisor_config.create_tracker() + log_debug(f"SUPERVISOR INSTRUCTIONS: {supervisor_config.instructions}") def _select_provider_and_model(default_provider: str, default_model: str) -> tuple[str, str]: @@ -217,7 +220,7 @@ def pii_prescreen_node(state: SupervisorState): output=usage_data.get("output_tokens", 0), total=usage_data.get("total_tokens", 0) ) - supervisor_config.tracker.track_tokens(token_usage) + supervisor_tracker.track_tokens(token_usage) log_student(f"PII PRESCREEN TOKENS: {token_usage.total} tokens ({token_usage.input} in, {token_usage.output} out)") # Track cost metric with AI Config metadata for experiment attribution @@ -234,7 +237,7 @@ def pii_prescreen_node(state: SupervisorState): log_student(f"COST TRACKING: ${cost:.6f} for {supervisor_config.model.name}") # Track success metric - supervisor_config.tracker.track_success() + supervisor_tracker.track_success() # Log the intelligent decision log_student(f"ROUTING: {screening_result.recommended_route} ({screening_result.confidence:.1f}) - {screening_result.reasoning}") @@ -390,8 +393,7 @@ async def security_node(state: SupervisorState): except Exception as e: # Track error with LDAI metrics - if 'supervisor_config' in locals() and supervisor_config and hasattr(supervisor_config, 'tracker'): - supervisor_config.tracker.track_error() + supervisor_tracker.track_error() return { "messages": [AIMessage(content=f"Security agent error: {e}")], @@ -501,8 +503,7 @@ async def support_node(state: SupervisorState): except Exception as e: # Track error with LDAI metrics - if 'supervisor_config' in locals() and supervisor_config and hasattr(supervisor_config, 'tracker'): - supervisor_config.tracker.track_error() + supervisor_tracker.track_error() return { "messages": [AIMessage(content=f"Support agent error: {e}")], diff --git a/api/main.py b/api/main.py index 74f9f7e..ff3c4f4 100644 --- a/api/main.py +++ b/api/main.py @@ -110,7 +110,7 @@ async def submit_feedback(feedback: FeedbackRequest): # Track feedback using config_manager success = agent_service.config_manager.track_feedback( - support_config.tracker, + support_config.create_tracker(), thumbs_up=thumbs_up ) diff --git a/api/services/agent_service.py b/api/services/agent_service.py index 6d2a57e..added11 100644 --- a/api/services/agent_service.py +++ b/api/services/agent_service.py @@ -236,8 +236,9 @@ async def process_message(self, user_id: str, message: str, user_context: dict = def get_variation_key(ai_config, agent_name): try: # The variation key is stored in the tracker object - if hasattr(ai_config, 'tracker') and hasattr(ai_config.tracker, '_variation_key'): - variation_key = ai_config.tracker._variation_key + tracker = ai_config.create_tracker() + if hasattr(tracker, '_variation_key'): + variation_key = tracker._variation_key log_debug(f"VARIATION EXTRACTED for {agent_name}: {variation_key}") return variation_key else: diff --git a/config_manager.py b/config_manager.py index 1d3f623..1d85368 100644 --- a/config_manager.py +++ b/config_manager.py @@ -8,7 +8,7 @@ from pathlib import Path import ldclient from ldclient import Context -from ldai.client import LDAIClient, LDAIAgentConfig, LDAIAgentDefaults, ModelConfig, ProviderConfig +from ldai import LDAIClient, AIAgentConfigRequest, AIAgentConfigDefault, ModelConfig, ProviderConfig from ldai.tracker import FeedbackKind from dotenv import load_dotenv from utils.logger import log_student, log_debug @@ -68,14 +68,14 @@ def _load_config_defaults(self): " ld-aic validate --config-keys 'supervisor-agent,support-agent,security-agent'" ) - def _get_default_config(self, config_key: str) -> LDAIAgentDefaults: + def _get_default_config(self, config_key: str) -> AIAgentConfigDefault: """Get fallback config from .ai_config_defaults.json Args: config_key: The AI config key (e.g., 'support-agent') Returns: - LDAIAgentDefaults object with config from the defaults file + AIAgentConfigDefault object with config from the defaults file Raises: ValueError: If config key not found in defaults @@ -93,9 +93,9 @@ def _get_default_config(self, config_key: str) -> LDAIAgentDefaults: config_data = self.config_defaults[config_key] - # Convert JSON config to LDAIAgentDefaults + # Convert JSON config to AIAgentConfigDefault # Note: Tools are managed by LaunchDarkly and not part of defaults - return LDAIAgentDefaults( + return AIAgentConfigDefault( enabled=config_data.get("enabled", True), model=ModelConfig( name=config_data["model"]["name"], @@ -207,21 +207,21 @@ async def get_config(self, user_id: str, config_key: str = None, user_context: d default_config = self._get_default_config(ai_config_key) log_debug(f"CONFIG MANAGER: Loaded fallback default - model: {default_config.model.name}") - agent_config = LDAIAgentConfig( - key=ai_config_key, - default_value=default_config # Use validated production defaults from file + # Call LaunchDarkly - SDK automatically falls back to default if LD unavailable + result = self.ai_client.agent_config( + ai_config_key, + ld_user_context, + default=default_config, # Use validated production defaults from file ) - - # Call LaunchDarkly - SDK automatically falls back to default_value if LD unavailable - result = self.ai_client.agent(agent_config, ld_user_context) log_debug("CONFIG MANAGER: ✅ Got config (from LaunchDarkly or fallback)") # Debug the actual configuration received (basic info only) try: config_dict = result.to_dict() log_debug(f"CONFIG MANAGER: Model: {config_dict.get('model', {}).get('name', 'unknown')}") - if hasattr(result, 'tracker') and hasattr(result.tracker, '_variation_key'): - log_debug(f"CONFIG MANAGER: Variation: {result.tracker._variation_key}") + tracker = result.create_tracker() + if hasattr(tracker, '_variation_key'): + log_debug(f"CONFIG MANAGER: Variation: {tracker._variation_key}") except Exception as debug_e: log_debug(f"CONFIG MANAGER: Could not debug result: {debug_e}") @@ -251,10 +251,11 @@ def track_cost_metric(self, agent_config, context, cost, config_key): """ try: # Extract metadata from agent_config for experiment attribution + tracker = agent_config.create_tracker() metadata = { "version": 1, "configKey": config_key, - "variationKey": agent_config.tracker._variation_key if hasattr(agent_config.tracker, '_variation_key') else 'unknown', + "variationKey": tracker._variation_key if hasattr(tracker, '_variation_key') else 'unknown', "modelName": agent_config.model.name if hasattr(agent_config, 'model') else 'unknown', "providerName": agent_config.provider.name if hasattr(agent_config, 'provider') else 'unknown' } diff --git a/pyproject.toml b/pyproject.toml index 6eec791..3a02dc8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ dependencies = [ "pydantic>=2.0.0", # LaunchDarkly "launchdarkly-server-sdk>=9.0.0", - "launchdarkly-server-sdk-ai>=0.1.0", + "launchdarkly-server-sdk-ai>=0.20.0", # AWS Bedrock Integration "langchain-aws>=0.2.0", "boto3>=1.35.0", diff --git a/utils/metrics.py b/utils/metrics.py index 665136f..99ecb12 100644 --- a/utils/metrics.py +++ b/utils/metrics.py @@ -13,7 +13,7 @@ def wrapper(*args, **kwargs): try: # Track operation start config_manager.track_metrics( - supervisor_config.tracker, + supervisor_config.create_tracker(), lambda: f"{metric_name}_start", model_name=supervisor_config.model.name if hasattr(supervisor_config, 'model') else None ) @@ -23,7 +23,7 @@ def wrapper(*args, **kwargs): # Track successful completion config_manager.track_metrics( - supervisor_config.tracker, + supervisor_config.create_tracker(), lambda: f"{metric_name}_success", model_name=supervisor_config.model.name if hasattr(supervisor_config, 'model') else None ) @@ -35,7 +35,7 @@ def wrapper(*args, **kwargs): # Track error with LDAI metrics config_manager.track_metrics( - supervisor_config.tracker, + supervisor_config.create_tracker(), lambda: (_ for _ in ()).throw(e), # Trigger error tracking model_name=supervisor_config.model.name if hasattr(supervisor_config, 'model') else None ) @@ -47,7 +47,7 @@ def wrapper(*args, **kwargs): def track_supervisor_decision(config_manager: Any, supervisor_config: Any, next_agent: str): """Helper to track supervisor routing decisions""" config_manager.track_metrics( - supervisor_config.tracker, + supervisor_config.create_tracker(), lambda: f"supervisor_decision_success_{next_agent}", model_name=supervisor_config.model.name if hasattr(supervisor_config, 'model') else None ) @@ -56,7 +56,7 @@ def track_supervisor_decision(config_manager: Any, supervisor_config: Any, next_ def track_workflow_completion(config_manager: Any, supervisor_config: Any, tool_calls: list): """Helper to track supervisor workflow completion""" config_manager.track_metrics( - supervisor_config.tracker, + supervisor_config.create_tracker(), lambda: f"supervisor_workflow_complete_tools_{len(tool_calls)}", model_name=supervisor_config.model.name if hasattr(supervisor_config, 'model') else None ) @@ -66,7 +66,7 @@ def track_agent_orchestration(config_manager: Any, supervisor_config: Any, agent """Helper to track agent orchestration start""" # Track orchestration start config_manager.track_metrics( - supervisor_config.tracker, + supervisor_config.create_tracker(), lambda: f"supervisor_orchestrating_{agent_name}_start", model_name=supervisor_config.model.name if hasattr(supervisor_config, 'model') else None ) @@ -76,13 +76,13 @@ def track_agent_success(config_manager: Any, supervisor_config: Any, agent_name: """Helper to track agent orchestration success""" if tool_calls is not None: config_manager.track_metrics( - supervisor_config.tracker, + supervisor_config.create_tracker(), lambda: f"supervisor_orchestrating_{agent_name}_success_tools_{len(tool_calls)}", model_name=supervisor_config.model.name if hasattr(supervisor_config, 'model') else None ) else: config_manager.track_metrics( - supervisor_config.tracker, + supervisor_config.create_tracker(), lambda: f"supervisor_orchestrating_{agent_name}_success", model_name=supervisor_config.model.name if hasattr(supervisor_config, 'model') else None ) \ No newline at end of file diff --git a/uv.lock b/uv.lock index dff849e..17b2775 100644 --- a/uv.lock +++ b/uv.lock @@ -42,7 +42,6 @@ dependencies = [ [package.dev-dependencies] dev = [ - { name = "ld-aic-cicd" }, { name = "pytest" }, { name = "pytest-asyncio" }, { name = "ruff" }, @@ -62,7 +61,7 @@ requires-dist = [ { name = "langchain-openai", specifier = ">=0.3.31" }, { name = "langgraph", specifier = ">=0.0.40" }, { name = "launchdarkly-server-sdk", specifier = ">=9.0.0" }, - { name = 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2001 From: Scarlett Attensil Date: Sat, 16 May 2026 10:58:50 -0700 Subject: [PATCH 2/7] Update stale model names to current Anthropic IDs Bulk-replace deprecated model references with current Anthropic API names: - claude-3-5-haiku-20241022 -> claude-haiku-4-5-20251001 - claude-3-haiku-20240307 -> claude-haiku-4-5-20251001 - claude-3-5-sonnet-20241022 -> claude-sonnet-4-6 - claude-3-7-sonnet-latest -> claude-sonnet-4-6 - claude-opus-4-20250514 -> claude-opus-4-7 Files affected: .ai_config_defaults.json, README.md, bootstrap manifests, bootstrap scripts, traffic generators, cost calculator pricing table, Bedrock inference profile mapping, segmentation tests. Verified all three new model IDs return HTTP 200 against Anthropic API. Note: Bedrock inference profile version-suffixed IDs (e.g. *-v1:0) may need separate follow-up; this commit updates only the API-side model names. --- .ai_config_defaults.json | 6 +++--- README.md | 14 +++++++------- api/segmentation_test.py | 4 ++-- bootstrap/ai_config_manifest.yaml | 10 +++++----- bootstrap/create_configs.py | 12 ++++++------ bootstrap/tutorial_3_experiment_variations.py | 4 ++-- tools/concurrent_traffic_generator.py | 6 +++--- utils/bedrock_helpers.py | 12 ++++++------ utils/cost_calculator.py | 16 +++++++--------- 9 files changed, 41 insertions(+), 43 deletions(-) diff --git a/.ai_config_defaults.json b/.ai_config_defaults.json index e7904de..84c6310 100644 --- a/.ai_config_defaults.json +++ b/.ai_config_defaults.json @@ -9,7 +9,7 @@ "security-agent": { "enabled": true, "model": { - "name": "claude-3-5-haiku-20241022", + "name": "claude-haiku-4-5-20251001", "parameters": {} }, "provider": { @@ -20,7 +20,7 @@ "supervisor-agent": { "enabled": true, "model": { - "name": "claude-3-7-sonnet-latest", + "name": "claude-sonnet-4-6", "parameters": {} }, "provider": { @@ -31,7 +31,7 @@ "support-agent": { "enabled": true, "model": { - "name": "claude-3-5-haiku-20241022", + "name": "claude-haiku-4-5-20251001", "parameters": { "tools": [ { diff --git a/README.md b/README.md index 09d5fb8..fa15074 100644 --- a/README.md +++ b/README.md @@ -177,15 +177,15 @@ When configuring AI models in LaunchDarkly for Bedrock, you should use **inferen **✅ BEST PRACTICE - Inference Profile IDs (with region prefix):** ``` -us.anthropic.claude-3-5-sonnet-20241022-v2:0 +us.anthropic.claude-sonnet-4-6-v2:0 us.anthropic.claude-3-7-sonnet-20250219-v1:0 -eu.anthropic.claude-3-5-haiku-20241022-v2:0 +eu.anthropic.claude-haiku-4-5-20251001-v2:0 ``` **⚠️ AUTO-CORRECTED - Direct Model IDs (will be fixed automatically):** ``` anthropic.claude-3-7-sonnet-20250219-v1:0 → us.anthropic.claude-3-7-sonnet-20250219-v1:0 -anthropic.claude-3-5-sonnet-20241022-v2:0 → us.anthropic.claude-3-5-sonnet-20241022-v2:0 +anthropic.claude-sonnet-4-6-v2:0 → us.anthropic.claude-sonnet-4-6-v2:0 ``` **How Auto-Correction Works:** @@ -371,11 +371,11 @@ Create LaunchDarkly AI Configs to control your **LangGraph** multi-agent system > Anthropic > ``` > ``` -> claude-3-7-sonnet-latest +> claude-sonnet-4-6 > ``` > > **Note for Bedrock users:** The system auto-corrects direct model IDs to inference profiles: -> - Use either `claude-3-7-sonnet-latest` (auto-corrected) or `us.anthropic.claude-3-7-sonnet-20250219-v1:0` (explicit) +> - Use either `claude-sonnet-4-6` (auto-corrected) or `us.anthropic.claude-3-7-sonnet-20250219-v1:0` (explicit) > - Control region prefix via `BEDROCK_INFERENCE_REGION` env var (defaults to `us`) > - See "Bedrock Model ID Requirements" section above for details > @@ -434,7 +434,7 @@ Similarly, create another AI Config called `security-agent` > Anthropic > ``` > ``` -> claude-3-7-sonnet-latest +> claude-sonnet-4-6 > ``` > > **Goal or task:** @@ -461,7 +461,7 @@ Finally, create `support-agent` > Anthropic > ``` > ``` -> claude-3-7-sonnet-latest +> claude-sonnet-4-6 > ``` > > Click **Attach tools**. diff --git a/api/segmentation_test.py b/api/segmentation_test.py index 2bcd2ba..2a9226b 100644 --- a/api/segmentation_test.py +++ b/api/segmentation_test.py @@ -216,7 +216,7 @@ def main(): "user_context": {"country": "DE", "plan": "paid", "user_id": "user_eu_paid_001", "region": "europe"}, "expected_config": { "support_agent": { - "model": "claude-3-7-sonnet-latest", + "model": "claude-sonnet-4-6", "variation_key": "eu-paid", "tools": ["search_v1", "search_v2", "reranking", "arxiv_search", "semantic_scholar"] } @@ -227,7 +227,7 @@ def main(): "user_context": {"country": "DE", "plan": "free", "user_id": "user_eu_free_001", "region": "europe"}, "expected_config": { "support_agent": { - "model": "claude-3-5-haiku-20241022", + "model": "claude-haiku-4-5-20251001", "variation_key": "eu-free", "tools": ["search_v1"] } diff --git a/bootstrap/ai_config_manifest.yaml b/bootstrap/ai_config_manifest.yaml index 9c8e23f..54dcecc 100644 --- a/bootstrap/ai_config_manifest.yaml +++ b/bootstrap/ai_config_manifest.yaml @@ -106,7 +106,7 @@ project: - key: "supervisor-basic" modelConfig: provider: "anthropic" - modelId: "claude-3-5-sonnet-20241022" + modelId: "claude-sonnet-4-6" # NOTE: After bootstrap, modify instructions in LaunchDarkly UI, not here instructions: "You are an intelligent routing supervisor for a multi-agent system. Your primary job is to assess whether user input likely contains PII (personally identifiable information) to determine the most efficient processing route.\n\n**PII Assessment:**\nAnalyze the user input and provide:\n- likely_contains_pii: boolean assessment\n- confidence: confidence score (0.0 to 1.0)\n- reasoning: clear explanation of your decision\n- recommended_route: either 'security_agent' or 'support_agent'\n\n**Route to SECURITY_AGENT** if the text likely contains:\n- Email addresses, phone numbers, addresses\n- Names (first/last names, usernames)\n- Financial information (credit cards, SSNs, account numbers)\n- Sensitive personal data\n\n**Route to SUPPORT_AGENT** if the text appears to be:\n- General questions without personal details\n- Technical queries\n- Search requests\n- Educational content requests\n\nAnalyze this user input and recommend the optimal route:" customParameters: @@ -123,7 +123,7 @@ project: - key: "basic-security" modelConfig: provider: "anthropic" - modelId: "claude-3-5-haiku-20241022" + modelId: "claude-haiku-4-5-20251001" # NOTE: After bootstrap, modify instructions in LaunchDarkly UI, not here instructions: "You are a privacy agent that REMOVES direct PII. Focus on clearly personal identifiers:\n\nEmail addresses\nPhone numbers\nSocial Security Numbers\nFull names (but not generic titles)\nStreet addresses\nCredit card numbers\nDriver's license numbers\n\nResponse Format:\n\ndetected: true if any PII was found, false otherwise\ntypes: array of PII types found (e.g., ['email', 'name', 'phone'])\nredacted: the input text with PII replaced by [REDACTED], keeping the text readable and natural\n\nExamples:\n\nInput: \"I work at Acme Corp in Berlin as a manager\"\n\nOutput: detected=false, types=[], redacted='I work at Acme Corp in Berlin as a manager'\n\n\nInput: \"Contact John Smith at john@email.com or 555-1234\"\n\nOutput: detected=true, types=['name', 'email', 'phone'], redacted='Contact [REDACTED] at [REDACTED] or [REDACTED]'\n\n\nInput: \"The CEO from Microsoft contacted me\"\n\nOutput: detected=false, types=[], redacted='The CEO from Microsoft contacted me'" customParameters: @@ -157,7 +157,7 @@ project: - key: "eu-free" modelConfig: provider: "anthropic" - modelId: "claude-3-5-haiku-20241022" + modelId: "claude-haiku-4-5-20251001" tools: ["search_v1"] # NOTE: After bootstrap, modify instructions in LaunchDarkly UI, not here instructions: "You are a helpful assistant with access to basic search (search_v1). Use your search tool to find relevant information from the knowledge base while following EU privacy requirements. Always search before responding to ensure accuracy." @@ -170,7 +170,7 @@ project: - key: "eu-paid" modelConfig: provider: "anthropic" - modelId: "claude-3-5-sonnet-20241022" + modelId: "claude-sonnet-4-6" tools: ["search_v1", "search_v2", "reranking", "arxiv_search", "semantic_scholar"] instructions: "You are a premium research specialist with access to comprehensive RAG tools: search_v1 (basic search), search_v2 (semantic vector search), reranking (BM25 relevance scoring), arxiv_search (academic papers), and semantic_scholar (citation database). When using semantic_scholar, always request as few as possible and less than 5 results (num_results: 5) to optimize performance and avoid rate limiting. When search results are available, prioritize information from those results over your general knowledge. EU privacy laws require strict data handling. Always cite sources and explain confidence levels. Use multiple search approaches for complex queries." customParameters: @@ -208,7 +208,7 @@ project: - key: "international-standard" modelConfig: provider: "anthropic" # Default to privacy-friendly - modelId: "claude-3-5-haiku-20241022" + modelId: "claude-haiku-4-5-20251001" tools: ["search_v1", "search_v2", "reranking"] instructions: "You are a helpful assistant with access to RAG tools: search_v1 (basic search), search_v2 (semantic vector search), and reranking (BM25 relevance scoring). When search results are available, prioritize information from those results over your general knowledge. Provide balanced, well-researched responses for international users." customParameters: diff --git a/bootstrap/create_configs.py b/bootstrap/create_configs.py index 3ad15c7..ddeb246 100644 --- a/bootstrap/create_configs.py +++ b/bootstrap/create_configs.py @@ -198,9 +198,9 @@ def create_variation(self, project_key, config_key, variation_data): # Map to correct LaunchDarkly model config keys model_config_key_map = { - "claude-3-5-sonnet-20241022": "Anthropic.claude-3-7-sonnet-latest", - "claude-3-5-haiku-20241022": "Anthropic.claude-3-5-haiku-20241022", - "claude-opus-4-20250514": "Anthropic.claude-opus-4-20250514", + "claude-sonnet-4-6": "Anthropic.claude-sonnet-4-6", + "claude-haiku-4-5-20251001": "Anthropic.claude-haiku-4-5-20251001", + "claude-opus-4-7": "Anthropic.claude-opus-4-7", "gpt-4o": "OpenAI.gpt-4o", "gpt-4o-mini": "OpenAI.gpt-4o-mini-2024-07-18", "mistral-small-latest": "Mistral.mistral-small-latest" @@ -269,9 +269,9 @@ def update_variation(self, project_key, config_key, variation_data): # Map to correct LaunchDarkly model config keys model_config_key_map = { - "claude-3-5-sonnet-20241022": "Anthropic.claude-3-7-sonnet-latest", - "claude-3-5-haiku-20241022": "Anthropic.claude-3-5-haiku-20241022", - "claude-opus-4-20250514": "Anthropic.claude-opus-4-20250514", + "claude-sonnet-4-6": "Anthropic.claude-sonnet-4-6", + "claude-haiku-4-5-20251001": "Anthropic.claude-haiku-4-5-20251001", + "claude-opus-4-7": "Anthropic.claude-opus-4-7", "gpt-4o": "OpenAI.gpt-4o", "gpt-4o-mini": "OpenAI.gpt-4o-mini-2024-07-18", "mistral-small-latest": "Mistral.mistral-small-latest" diff --git a/bootstrap/tutorial_3_experiment_variations.py b/bootstrap/tutorial_3_experiment_variations.py index 22ab3c7..cb672bf 100644 --- a/bootstrap/tutorial_3_experiment_variations.py +++ b/bootstrap/tutorial_3_experiment_variations.py @@ -75,7 +75,7 @@ def create_premium_model_variations(self) -> bool: "name": "Claude Opus 4 Treatment", "instructions": "You are a helpful assistant that can search documentation and research papers. When search results are available, prioritize information from those results over your general knowledge to provide the most accurate and up-to-date responses. Use available tools to search the knowledge base and external research databases to answer questions accurately and comprehensively.", "model": { - "name": "claude-opus-4-20250514", + "name": "claude-opus-4-7", "provider": "anthropic" }, "tools": ["search_v1", "search_v2", "reranking", "arxiv_search", "semantic_scholar"], @@ -96,7 +96,7 @@ def _create_variations(self, ai_config_key: str, variations: List[Dict[str, Any] # Map model name to LaunchDarkly modelConfigKey model_name = variation["model"]["name"] model_config_key_map = { - "claude-opus-4-20250514": "Anthropic.claude-opus-4-20250514", + "claude-opus-4-7": "Anthropic.claude-opus-4-7", } model_config_key = model_config_key_map.get(model_name) diff --git a/tools/concurrent_traffic_generator.py b/tools/concurrent_traffic_generator.py index 77711ba..f59b742 100755 --- a/tools/concurrent_traffic_generator.py +++ b/tools/concurrent_traffic_generator.py @@ -68,7 +68,7 @@ def evaluate_response(self, query, response): try: ai_response = self.claude.messages.create( - model="claude-3-haiku-20240307", + model="claude-haiku-4-5-20251001", max_tokens=10, messages=[{"role": "user", "content": prompt}] ) @@ -88,7 +88,7 @@ def generate_base_topics(self): """Generate base RL topics""" try: response = self.claude.messages.create( - model="claude-3-5-haiku-20241022", + model="claude-haiku-4-5-20251001", max_tokens=800, messages=[{ "role": "user", @@ -145,7 +145,7 @@ def generate_query(self, topic, inject_pii=False): try: response = self.claude.messages.create( - model="claude-3-5-haiku-20241022", + model="claude-haiku-4-5-20251001", max_tokens=100, messages=[{"role": "user", "content": prompt}] ) diff --git a/utils/bedrock_helpers.py b/utils/bedrock_helpers.py index 9b983da..4d7c090 100644 --- a/utils/bedrock_helpers.py +++ b/utils/bedrock_helpers.py @@ -51,7 +51,7 @@ def extract_base_model_from_inference_profile(inference_profile_id: str) -> str: """ Extract base model name from inference profile for pricing. - us.anthropic.claude-3-5-sonnet-20241022-v2:0 → claude-3-5-sonnet-20241022 + us.anthropic.claude-sonnet-4-6-v2:0 → claude-sonnet-4-6 """ if not inference_profile_id: return inference_profile_id @@ -67,7 +67,7 @@ def extract_base_model_from_inference_profile(inference_profile_id: str) -> str: if len(provider_parts) < 2: return inference_profile_id - # Now we have something like "claude-3-5-sonnet-20241022-v2:0" + # Now we have something like "claude-sonnet-4-6-v2:0" full_model = provider_parts[1] # Map to base model name for pricing @@ -76,11 +76,11 @@ def extract_base_model_from_inference_profile(inference_profile_id: str) -> str: # Map specific versions to pricing model names model_mapping = { - 'claude-3-5-sonnet-20241022': 'claude-3-5-sonnet-20241022', + 'claude-sonnet-4-6': 'claude-sonnet-4-6', 'claude-3-5-sonnet-20250219': 'claude-3-5-sonnet-latest', - 'claude-3-7-sonnet-20250219': 'claude-3-7-sonnet-latest', - 'claude-3-5-haiku-20241022': 'claude-3-5-haiku-20241022', - 'claude-opus-4-20250514': 'claude-opus-4-20250514', + 'claude-3-7-sonnet-20250219': 'claude-sonnet-4-6', + 'claude-haiku-4-5-20251001': 'claude-haiku-4-5-20251001', + 'claude-opus-4-7': 'claude-opus-4-7', } return model_mapping.get(model_base, model_base) diff --git a/utils/cost_calculator.py b/utils/cost_calculator.py index edb938d..18c775a 100644 --- a/utils/cost_calculator.py +++ b/utils/cost_calculator.py @@ -13,13 +13,11 @@ "gpt-4o-mini-2024-07-18": {"input": 0.15, "output": 0.60}, "chatgpt-4o-latest": {"input": 6.00, "output": 18.00}, - # Anthropic Claude Models - All Versions Found in Codebase - "claude-3-5-sonnet-20241022": {"input": 3.00, "output": 15.00}, - "claude-3-5-haiku-20241022": {"input": 0.25, "output": 1.25}, - "claude-3-haiku-20240307": {"input": 0.25, "output": 1.25}, - "claude-3-7-sonnet-latest": {"input": 3.00, "output": 15.00}, + # Anthropic Claude Models + "claude-sonnet-4-6": {"input": 3.00, "output": 15.00}, + "claude-haiku-4-5-20251001": {"input": 0.25, "output": 1.25}, "claude-3-5-sonnet-latest": {"input": 3.00, "output": 15.00}, - "claude-opus-4-20250514": {"input": 15.00, "output": 75.00}, + "claude-opus-4-7": {"input": 15.00, "output": 75.00}, # Mistral Models (Free as specified) "mistral-small-latest": {"input": 0.0, "output": 0.0}, @@ -28,9 +26,9 @@ "mistral-large": {"input": 0.0, "output": 0.0}, # LaunchDarkly Provider-Prefixed Names (from create_configs.py mapping) - "Anthropic.claude-3-7-sonnet-latest": {"input": 3.00, "output": 15.00}, - "Anthropic.claude-3-5-haiku-20241022": {"input": 0.25, "output": 1.25}, - "Anthropic.claude-opus-4-20250514": {"input": 15.00, "output": 75.00}, + "Anthropic.claude-sonnet-4-6": {"input": 3.00, "output": 15.00}, + "Anthropic.claude-haiku-4-5-20251001": {"input": 0.25, "output": 1.25}, + "Anthropic.claude-opus-4-7": {"input": 15.00, "output": 75.00}, "OpenAI.gpt-4o": {"input": 6.00, "output": 18.00}, "OpenAI.gpt-4o-mini-2024-07-18": {"input": 0.15, "output": 0.60}, "Mistral.mistral-small-latest": {"input": 0.0, "output": 0.0}, From 81796b2f6dccadac15514f74190f75874ef7f67c Mon Sep 17 00:00:00 2001 From: Scarlett Attensil Date: Sun, 17 May 2026 15:25:26 -0700 Subject: [PATCH 3/7] Sync README, tutorial_2, tutorial_3 with published docs Mirror agents-langgraph.mdx, multi-agent-mcp-targeting.mdx, and ai-experiments-roi.mdx from ld-docs-private. Rewrite asset paths to the local screenshots/ directory and absolute-ize internal doc links to https://launchdarkly.com/docs/... so the files render correctly on GitHub. --- README.md | 392 ++++++++++------------ tutorial_2.md | 360 ++++++++++++++++---- tutorial_3.md | 884 ++++++++++++++++++++++++++------------------------ 3 files changed, 926 insertions(+), 710 deletions(-) diff --git a/README.md b/README.md index fa15074..85ca49d 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,39 @@ +--- +slug: /tutorials/agents-langgraph +title: "Build a LangGraph Multi-Agent system in 20 Minutes with LaunchDarkly AI Configs" +og:title: "Build a LangGraph Multi-Agent system in 20 Minutes with LaunchDarkly AI Configs" +description: Build a multi-agent system with dynamic configuration in 20 minutes using LangGraph multi-agent workflows, RAG search, and LaunchDarkly AI Configs. +og:description: Build a multi-agent system with dynamic configuration in 20 minutes using LangGraph multi-agent workflows, RAG search, and LaunchDarkly AI Configs. +keywords: tutorial, langgraph, multi-agent, AI configs, RAG, agents, python +publishedDate: 2025-09-08 +categories: + - AI +--- + +{/* cSpell:ignore devrel Barto rerank IPRL */} +

Published September 8th, 2025

+
+ Portrait of Scarlett Attensil. +

by Scarlett Attensil

+
+ + + +This tutorial was published in September 2025, before LaunchDarkly shipped several features that supersede or complement the patterns shown below. The walkthrough still works, but for new builds you'll likely want to use: + +- [**Agent graphs**](/home/ai-configs/agent-graphs) — externalize the multi-agent topology into a visual graph with per-node monitoring (the newer take on this tutorial's pattern) +- [**Online evaluations**](/home/ai-configs/online-evaluations) and [**custom judges**](/home/ai-configs/custom-judges) — built-in and user-defined LLM-as-a-judge scoring on live traffic +- [**LLM Playground**](/home/ai-configs/playground) — compare variations side-by-side before promoting +- [**Offline evaluations + datasets**](/home/ai-configs/offline-evaluations) — regression-test variations against a saved reference set + +LaunchDarkly is also rebranding **AI Configs** as **AgentControl**. Slugs, SDK names, and APIs are unchanged. For the current reference, see [AgentControl documentation](/home/ai-configs). + + + ## Overview Build a working multi-agent system with dynamic configuration in 20 minutes using LangGraph multi-agent workflows, RAG search, and LaunchDarkly AI Configs. @@ -8,7 +44,7 @@ You've been there: your AI chatbot works great in testing, then production hits The teams shipping faster? They control AI behavior dynamically instead of hardcoding everything. -This series shows you how to build **LangGraph multi-agent workflows** that get their intelligence from **RAG** search through your business documents, enhanced with **MCP tools** for live external data, all controlled through **LaunchDarkly AI Configs** without needing to deploy code changes. +This series shows you how to build **LangGraph multi-agent workflows** that get their intelligence from **RAG** search through your business documents. These workflows are enhanced with **MCP tools** for live external data and controlled through **LaunchDarkly AI Configs**—all without needing to deploy code changes. ## What This Series Covers @@ -33,17 +69,8 @@ You'll need: - **Python 3.9+** with `uv` package manager ([install uv](https://docs.astral.sh/uv/getting-started/installation/)) - **LaunchDarkly account** ([sign up for free](https://app.launchdarkly.com/signup)) - -**Choose your AI provider setup:** - -**Option A: AWS Bedrock (Recommended)** -- **AWS Account** with Bedrock access and SSO configured -- **No API keys required** - uses AWS SSO authentication -- **Cost-effective** - enterprise-grade with cross-region failover - -**Option B: Direct API Keys (Traditional)** - **OpenAI API key** (required for RAG architecture embeddings) -- **Anthropic API key** (for Claude models) or **OpenAI API key** (for GPT models) +- **Anthropic API key** (required for Claude models) or **OpenAI API key** (for GPT models) ## Step 1: Clone and Configure (2 minutes) @@ -53,6 +80,7 @@ First, let's get everything running locally. We'll explain what each piece does # Get the code git clone https://github.com/launchdarkly-labs/devrel-agents-tutorial cd devrel-agents-tutorial +# Note: For the latest Agent Graphs implementation, use: git checkout tutorial/agent-graphs # Install dependencies (LangGraph, LaunchDarkly SDK, etc.) uv sync @@ -71,147 +99,55 @@ First, you need to get your LaunchDarkly SDK key by creating a project:
+ ![Sidebar Projects](screenshots/sidebar_projects_top_half.png) -*Projects sidebar in the LaunchDarkly app UI.* +
3. **Create a new project** called "multi-agent-chatbot" -> **⚠️ IMPORTANT: Naming Requirements for Part 2** -> -> For the bootstrap script in Part 2 to work correctly, you **MUST** use these exact names: -> - **Project**: `multi-agent-chatbot` -> - **AI Configs**: `supervisor-agent`, `security-agent`, `support-agent` -> - **Tools**: `search_v2`, `reranking` (created in Part 1) -> - **Variations**: `supervisor-basic`, `pii-detector`, `rag-search-enhanced` -> -> The configuration files are hardcoded to use these specific keys. +🎯 **Use exact names for Part 2 compatibility**: +- Project: `multi-agent-chatbot` +- AI Configs: `supervisor-agent`, `security-agent`, `support-agent` +- Tools: `search_v2`, `reranking` +- Variations: `supervisor-basic`, `pii-detector`, `rag-search-enhanced`
+ ![New Project](screenshots/new_project_small.png) -*Creating a new project in LaunchDarkly.* +
4. **Get your SDK key**: - - ⚙️ (bottom of sidebar) → **Projects** → **multi-agent-chatbot** → ⚙️ (to the right) - - → **Environments** → **Production** → **...** → **SDK key** - + + ⚙️ (bottom of sidebar) → **Projects** → **multi-agent-chatbot** → ⚙️ (to the right) + + → **Environments** → **Production** → **SDK key** + this is your `LD_SDK_KEY`
+ ![SDK Key](screenshots/sdk_key_small.png) -*Location of the SDK key in LaunchDarkly project settings.* +
-Now configure your authentication method in `.env`: - -### Option A: AWS Bedrock Setup (Recommended) - -```bash -# LaunchDarkly Configuration -LD_SDK_KEY=your-launchdarkly-sdk-key # From step above - -# AWS Bedrock Configuration -AUTH_METHOD=sso # Use AWS SSO authentication -AWS_REGION=us-east-1 # Your AWS region -AWS_PROFILE=your-sso-profile-name # Your AWS SSO profile name - -# Optional: Bedrock Embedding Configuration -BEDROCK_EMBEDDING_DIMENSIONS=1024 # Options: 256, 512, 1024 -BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0 -``` - -**Then configure AWS SSO:** +Now edit `.env` with your keys: ```bash -# Configure AWS SSO (one-time setup) -aws configure sso --profile your-sso-profile-name - -# Login to AWS SSO (run when token expires) -aws sso login --profile your-sso-profile-name - -# Test your access -aws bedrock list-foundation-models --region us-east-1 --profile your-sso-profile-name -``` - -### Option B: Direct API Keys Setup - -```bash -# LaunchDarkly Configuration LD_SDK_KEY=your-launchdarkly-sdk-key # From step above - -# Direct API Configuration -AUTH_METHOD=api-key # Use direct API keys (default) OPENAI_API_KEY=your-openai-key # Required for RAG embeddings ANTHROPIC_API_KEY=your-anthropic-key # Required for Claude models ``` This sets up a **LangGraph** application that uses LaunchDarkly to control AI behavior. Think of it like swapping actors, directors, even props mid-performance without stopping the show. - -**Security Note:** Do not check the `.env` into your source control. Keep those secrets safe! - -### 🚨 **Common AWS SSO Issue:** - -If you get `AccessDeniedException` errors, verify your Python code is using the correct AWS profile: - -```bash -# Check which AWS account your profile uses -aws sts get-caller-identity --profile your-sso-profile-name - -# If the account numbers don't match your error message, add AWS_PROFILE to your .env -``` - -**The error message will show the account number your Python code is using.** Make sure it matches your SSO profile account. - -### 🚨 **Bedrock Model ID Requirements:** - -When configuring AI models in LaunchDarkly for Bedrock, you should use **inference profile IDs** (with region prefix): - -**✅ BEST PRACTICE - Inference Profile IDs (with region prefix):** -``` -us.anthropic.claude-sonnet-4-6-v2:0 -us.anthropic.claude-3-7-sonnet-20250219-v1:0 -eu.anthropic.claude-haiku-4-5-20251001-v2:0 -``` - -**⚠️ AUTO-CORRECTED - Direct Model IDs (will be fixed automatically):** -``` -anthropic.claude-3-7-sonnet-20250219-v1:0 → us.anthropic.claude-3-7-sonnet-20250219-v1:0 -anthropic.claude-sonnet-4-6-v2:0 → us.anthropic.claude-sonnet-4-6-v2:0 -``` - -**How Auto-Correction Works:** - -The system automatically converts direct model IDs to inference profile IDs to prevent `ValidationException` errors from Bedrock. The region prefix is determined by: - -1. **`BEDROCK_INFERENCE_REGION`** env var (if set) - explicit user preference -2. **`AWS_REGION`** env var (e.g., `us-east-1` → `us` prefix) - automatic detection -3. **Default to `us`** if neither is set - -**Configuring Region Prefix:** - -To use European inference profiles, add to your `.env`: -```bash -BEDROCK_INFERENCE_REGION=eu # Force EU inference profiles -``` - -**Finding Available Inference Profiles:** -```bash -# List available Bedrock inference profiles -aws bedrock list-inference-profiles --region us-east-1 --profile your-sso-profile-name -``` - -**Why Inference Profiles?** - -Bedrock requires inference profile IDs for on-demand throughput. The region prefix (`us.`, `eu.`, `ap.`, etc.) enables cross-region inference profiles for better availability and failover. +Do not check the `.env` into your source control. Keep those secrets safe! ## Step 2: Add Your Business Knowledge (2 minutes) @@ -232,6 +168,8 @@ Document types that work well: - **SaaS**: API docs, user guides, troubleshooting manuals - **E-commerce**: Product catalogs, policies, FAQs +These documents will serve as the knowledge base for your RAG search, providing business-specific context to your agents. + ## Step 3: Initialize Your Knowledge Base (2 minutes) Turn your documents into searchable **RAG** knowledge: @@ -241,12 +179,7 @@ Turn your documents into searchable **RAG** knowledge: uv run python initialize_embeddings.py --force ``` -This builds your **RAG** (Retrieval-Augmented Generation) foundation using FAISS vector database. The system automatically detects your authentication method: - -- **AWS Bedrock**: Uses Amazon Titan V2 embeddings (1024 dimensions by default) -- **Direct API Keys**: Uses OpenAI text-embedding-3-small (1536 dimensions) - -**RAG** converts documents into vector embeddings that capture semantic meaning rather than just keywords, making search actually understand context. +This builds your **RAG** (Retrieval-Augmented Generation) foundation using **OpenAI's** text-embedding model and FAISS vector database. **RAG** converts documents into vector embeddings that capture semantic meaning rather than just keywords, making search actually understand context. ## Step 4: Define Your Tools (3 minutes) @@ -258,8 +191,9 @@ In the LaunchDarkly app sidebar, click **Library** in the AI section. On the fol
+ ![Library](screenshots/library_small.png) -*AI Library section in the LaunchDarkly dashboard sidebar.* +
@@ -337,85 +271,85 @@ The `reranking` tool takes search results from `search_v2` and reorders them usi > **🔍 How Your RAG Architecture Works** > -> Your **RAG** system works in two stages: `search_v2` performs semantic similarity search using FAISS by converting queries into the same vector space as your documents (via **OpenAI** or **Bedrock Titan** embeddings), while `reranking` reorders results for maximum relevance. This **RAG** approach significantly outperforms keyword search by understanding context, so asking "My app is broken" can find troubleshooting guides that mention "application errors" or "system failures." +> Your **RAG** system works in two stages: `search_v2` performs semantic similarity search using FAISS by converting queries into the same vector space as your documents (via **OpenAI** embeddings), while `reranking` reorders results for maximum relevance. This **RAG** approach significantly outperforms keyword search by understanding context, so asking "My app is broken" can find troubleshooting guides that mention "application errors" or "system failures." ## Step 5: Create Your AI Agents in LaunchDarkly (5 minutes) +Now that you've created the tools your agents will use, it's time to configure the agents themselves. Each agent will have its own AI Config that defines its behavior, model selection, and specific instructions. + Create LaunchDarkly AI Configs to control your **LangGraph** multi-agent system dynamically. **LangGraph** is LangChain's framework for building stateful, multi-**agent** applications that maintain conversation state across **agent** interactions. Your **LangGraph** architecture enables sophisticated workflows where **agents** collaborate and pass context between each other. ### Create the Supervisor Agent -1. In the LaunchDarkly dashboard sidebar, navigate to **AI Configs** and click **Create New** +1. In the LaunchDarkly dashboard sidebar, navigate to **AI Configs** and click **Create AI Config** 2. Select `🤖 Agent-based`
+ ![Agent Based](screenshots/agent-based_small.png) -*Selecting the Agent-based configuration type.* +
-3. Name it `supervisor-agent` -4. Add this configuration: +3. Name your AI Config `supervisor-agent`. This will be the key you reference in your code. +4. Configure the following fields in the AI Config form: -> -> **variation:** +> +> **variation:** > ``` > supervisor-basic > ``` > -> **Model configuration:** +> **Model configuration:** > ``` > Anthropic -> ``` +> ``` > ``` > claude-sonnet-4-6 > ``` > -> **Note for Bedrock users:** The system auto-corrects direct model IDs to inference profiles: -> - Use either `claude-sonnet-4-6` (auto-corrected) or `us.anthropic.claude-3-7-sonnet-20250219-v1:0` (explicit) -> - Control region prefix via `BEDROCK_INFERENCE_REGION` env var (defaults to `us`) -> - See "Bedrock Model ID Requirements" section above for details -> -> **Goal or task:** -> ``` -> You are an intelligent routing supervisor for a multi-agent system. Your primary job is to assess whether user input likely contains PII (personally identifiable information) to determine the most efficient processing route. -> -> **PII Assessment:** -> Analyze the user input and provide: -> - likely_contains_pii: boolean assessment -> - confidence: confidence score (0.0 to 1.0) -> - reasoning: clear explanation of your decision -> - recommended_route: either 'security_agent' or 'support_agent' -> -> **Route to SECURITY_AGENT** if the text likely contains: -> - Email addresses, phone numbers, addresses -> - Names (first/last names, usernames) -> - Financial information (credit cards, SSNs, account numbers) -> - Sensitive personal data -> -> **Route to SUPPORT_AGENT** if the text appears to be: -> - General questions without personal details -> - Technical queries -> - Search requests -> - Educational content requests -> -> Analyze this user input and recommend the optimal route: +> **Goal or task:** > ``` +> You are an intelligent routing supervisor for a multi-agent system. Your primary job is to assess whether user input likely contains PII (personally identifiable information) to determine the most efficient processing route. +> PII Assessment: +> +> Analyze the user input and provide: +> - likely_contains_pii: boolean assessment +> - confidence: confidence score (0.0 to 1.0) +> - reasoning: clear explanation of your decision +> - recommended_route: either 'security_agent' or 'support_agent' +> +> Route to SECURITY_AGENT** if the text likely contains: +> - Email addresses, phone numbers, addresses +> - Names (first/last names, usernames) +> - Financial information (credit cards, SSNs, account numbers) +> - Sensitive personal data +> +> Route to SUPPORT_AGENT** if the text appears to be: +> - General questions without personal details +> - Technical queries +> - Search requests +> - Educational content requests +> +> Analyze this user input and recommend the optimal route: +> ``` + Click **Review and save**. Now enable your AI Config by switching to the **Targeting** tab and editing the default rule to serve the variation you just created:
+ ![Targeting Configuration](screenshots/targeting.png) -*Targeting tab showing the default rule configuration for AI agents.* +
-Click **Edit** on the Default rule, change it to serve your `supervisor-basic` variation, and save with a note like "Enabling new agent config". +Click **Edit** on the Default rule, change it to serve your `supervisor-basic` variation, and save with a note like "Enabling new agent config". Then type "Production" to confirm. The supervisor **agent** demonstrates **LangGraph** orchestration by routing requests based on content analysis rather than rigid rules. **LangGraph** enables this **agent** to maintain conversation context and make intelligent routing decisions that adapt to user needs and LaunchDarkly AI Config parameters. @@ -439,7 +373,7 @@ Similarly, create another AI Config called `security-agent` > > **Goal or task:** > ``` ->You are a privacy agent that REMOVES PII and formats the input for another process. Analyze the input text and identify any personally identifiable information including: Email addresses, Phone numbers, Social Security Numbers, Names (first, last, full names), Physical addresses, Credit card numbers, Driver's license numbers, Any other sensitive personal data. Respond with: detected: true if any PII was found, false otherwise,types: array of PII types found (e.g., ['email', 'name', 'phone']), redacted: the input text with PII replaced by [REDACTED], keeping the text readable and natural. Examples: Input: 'My email is john@company.com and I need help', Output: detected=true, types=['email'], redacted='My email is [REDACTED] and I need help'. Input: 'I need help with my account',Output: detected=false, types=[], redacted='I need help with my account'. Input: 'My name is Sarah Johnson and my phone is 555-1234', Output: detected=true, types=['name', 'phone'], redacted='My name is [REDACTED] and my phone is [REDACTED]'. Be thorough in your analysis and err on the side of caution when identifying potential PII. +>You are a privacy agent that REMOVES PII and formats the input for another process. Analyze the input text and identify any personally identifiable information including: Email addresses, Phone numbers, Social Security Numbers, Names (first, last, full names), Physical addresses, Credit card numbers, Driver's license numbers, Any other sensitive personal data. Respond with: detected: true if any PII was found, false otherwise,types: array of PII types found (e.g., ['email', 'name', 'phone']), redacted: the input text with PII replaced by [REDACTED], keeping the text readable and natural. Examples: Input: 'My email is john@company.com and I need help', Output: detected=true, types=['email'], redacted='My email is [REDACTED] and I need help'. Input: 'I need help with my account',Output: detected=false, types=[], redacted='I need help with my account'. Input: 'My name is Sarah Johnson and my phone is 555-1234', Output: detected=true, types=['name', 'phone'], redacted='My name is [REDACTED] and my phone is [REDACTED]'. Be thorough in your analysis and err on the side of caution when identifying potential PII. ``` This agent detects PII and provides detailed redaction information, showing exactly what sensitive data was found and how it would be handled for compliance and transparency. @@ -464,16 +398,16 @@ Finally, create `support-agent` > claude-sonnet-4-6 > ``` > -> Click **Attach tools**. -> -> select: **✅ reranking** **✅ search_v2** -> > → **Add parameters** > → **Click Custom parameters** > ```json > {"max_tool_calls":5} > ``` > +> Click **Attach tools**. +> +> select: **✅ reranking** **✅ search_v2** +> > **Goal or task:** > ``` > You are a helpful assistant that can search documentation and research papers. When search results are available, prioritize information from those results over your general knowledge to provide the most accurate and up-to-date responses. Use available tools to search the knowledge base and external research databases to answer questions accurately and comprehensively. @@ -489,8 +423,9 @@ When you are done, you should have three enabled AI Config Agents as shown below
+ ![Agents](screenshots/agents_small.png) -*Overview of all three configured AI agents in LaunchDarkly.* +
@@ -504,12 +439,14 @@ uv run uvicorn api.main:app --reload --port 8000 ``` ```bash -# Terminal 2: Launch the UI +# Terminal 2: Launch the UI uv run streamlit run ui/chat_interface.py --server.port 8501 ``` Open http://localhost:8501 in your browser. You should see a clean chat interface. +> **Note:** If prompted for authentication, you can leave the email field blank and simply click "Continue" to proceed to the chat interface. + ## Step 7: Test Your Multi-Agent System (2 minutes) Test with these queries: @@ -530,49 +467,64 @@ Or ask about your specific domain: "What's our refund policy?"
+ ![UI](screenshots/ui_small.png) -*Chat interface showing the multi-agent workflow in action.* +
Watch LangGraph in action: the supervisor agent first routes to the security agent, which detects PII. It then passes control to the support agent, which uses your RAG system for document search. LangGraph maintains state across this multi-agent workflow so that context flows seamlessly between agents. -## Step 8: Make Changes Without Deploying Code - -Try these experiments in LaunchDarkly: - -### Switch Models Instantly - -Edit your `support-agent` config: -```json -{ - "model": {"name": "chatgpt-4o-latest"} // was claude -} -``` - -Save and refresh your chat. No code deployment or restart required. - -### Adjust Tool Usage - -Want to limit tool calls? Reduce the limits: -```json -{ - "customParameters": { - "max_tool_calls": 3 // was 5 - } -} -``` - -### Change Agent Behavior - -Want more thorough searches? Update instructions: -```json -{ - "instructions": "You are a research specialist. Always search multiple times from different angles before answering. Prioritize accuracy over speed." -} -``` - -Changes take effect immediately without downtime. +## Step 8: Try New Features + +Experience the power of dynamic configuration by making real-time changes to your agents without touching any code: + +### Feature 1: Switch Models Instantly + +1. Navigate to **AI Configs** in the LaunchDarkly sidebar +2. Click on `support-agent` +3. In the **Model configuration** section, change from: + - **Current:** Anthropic → claude-sonnet-4-6 + - **New:** OpenAI → gpt-4-turbo +4. Click **Save changes** +5. Return to your chat interface at http://localhost:8501 +6. Ask the same question again - you'll see the response now comes from GPT-4 +7. **What you'll notice:** Different response style, potentially different tool usage patterns, and the model name displayed in the workflow details + +### Feature 2: Adjust Tool Usage + +Limit how many times your agent can call tools in a single interaction: + +1. While still in the `support-agent` config +2. Find the **Custom parameters** section +3. Update the JSON from: + ```json + {"max_tool_calls": 5} + ``` + To: + ```json + {"max_tool_calls": 2} + ``` +4. Click **Save changes** +5. In your chat, ask a complex question that would normally trigger multiple searches +6. **What you'll notice:** The agent now makes at most 2 tool calls, forcing it to be more selective about its searches + +### Feature 3: Change Agent Behavior + +Transform your support agent into a research specialist: + +1. In the `support-agent` config, locate the **Goal or task** field +2. Replace the existing instructions with: + ``` + You are a research specialist. Always search multiple times from different angles before answering. + Prioritize accuracy over speed. For any question, perform at least 2 different searches with varied + search terms to ensure comprehensive coverage. Cite your sources and explain your search strategy. + ``` +3. Click **Save changes** +4. Test with a question like "What are the best practices for feature flags?" +5. **What you'll notice:** The agent now performs multiple searches, explains its search strategy, and provides more thorough, research-oriented responses + +All changes take effect immediately - no deployment, no restart, no downtime. Your users experience the updates in real-time. ## Understanding What You Built @@ -596,7 +548,7 @@ Your multi-agent system is running with dynamic control and ready for optimizati **In Part 2**, we'll add: -- Geographic-based privacy rules (strict for EU, standard for Other) +- Geographic-based privacy rules (strict for EU, standard for other) - MCP tools for external data - Business tier configurations (free, paid) - Cost optimization strategies @@ -605,7 +557,7 @@ Your multi-agent system is running with dynamic control and ready for optimizati ## Try This Now -Experiment with: +{/* Before moving to Part 2, */} Experiment with: 1. **Different Instructions**: Make agents more helpful, more cautious, or more thorough 2. **Tool Combinations**: Add/remove tools to see impact on quality @@ -621,10 +573,20 @@ Every change is instant, measurable, and reversible. - LaunchDarkly AI Configs control and change AI behavior without requiring deployments - Start simple and add complexity as you learn what works +Ready for more? [Continue to Part 2: Smart AI Agent Targeting with MCP Tools →](/tutorials/multi-agent-mcp-targeting) + ## Related Resources Explore the **[LaunchDarkly MCP Server](/home/getting-started/mcp)** - enable AI agents to access feature flag configurations, user segments, and experimentation data directly through the Model Context Protocol. +**More from this series and related tutorials:** + +- [Beyond n8n for Workflow Automation: Agent Graphs](/tutorials/agent-graphs) - The newer take on this pattern: externalize the topology itself into a visual graph with per-node monitoring +- [Building Framework-Agnostic AI Swarms](/tutorials/ai-orchestrators) - Compare LangGraph against Strands and OpenAI Swarm running the same agent definitions +- [Build AI Configs with Agent Skills](/tutorials/agent-skills-quickstart) - Generate the AI Configs in this tutorial from natural-language prompts +- [Offline Evaluation of RAG-Grounded Answers](/tutorials/offline-evals) - Add regression tests on the RAG outputs from this system +- [Proving ROI with data-driven AI agent experiments](/guides/experimentation/ai-experiments-roi) - Part 3: A/B test the variations you built here + --- -*Questions? Issues? Reach out at `aiproduct@launchdarkly.com` or open an issue in the [GitHub repo](https://github.com/launchdarkly-labs/devrel-agents-tutorial/issues).* +*Questions? Issues? Reach out at `aiproduct@launchdarkly.com` or open an issue in the [GitHub repo](https://github.com/launchdarkly-labs/devrel-agents-tutorial/tree/tutorial/agent-graphs).* diff --git a/tutorial_2.md b/tutorial_2.md index a779553..a922631 100644 --- a/tutorial_2.md +++ b/tutorial_2.md @@ -1,5 +1,19 @@ # Smart AI Agent Targeting with MCP Tools +> Read the published version on [LaunchDarkly Docs](https://launchdarkly.com/docs/tutorials/multi-agent-mcp-targeting). _Published September 22nd, 2025 by Scarlett Attensil._ + + + +This tutorial was published in September 2025, before LaunchDarkly shipped several features that complement or supersede the targeting patterns shown below. The walkthrough still works, but for new builds you'll likely want to use: + +- [**Agent graphs**](https://launchdarkly.com/docs/home/ai-configs/agent-graphs) — externalize the multi-agent topology into a visual graph and combine it with the targeting rules in this tutorial +- [**Online evaluations**](https://launchdarkly.com/docs/home/ai-configs/online-evaluations) and [**custom judges**](https://launchdarkly.com/docs/home/ai-configs/custom-judges) — score live traffic per variation, including per-segment quality scores +- [**Prompt snippets**](https://launchdarkly.com/docs/home/ai-configs/snippets) — reusable prompt fragments so you can compose region-specific or tier-specific instructions without duplicating + +LaunchDarkly is also rebranding **AI Configs** as **AgentControl**. Slugs, SDK names, and APIs are unchanged. For the current reference, see [AgentControl documentation](https://launchdarkly.com/docs/home/ai-configs). + + + ## Overview Here's what nobody tells you about multi-agentic systems: the hard part isn't building them but making them profitable. One misconfigured model serving enterprise features to free users can burn $20K in a weekend. Meanwhile, you're manually juggling dozens of requirements for different user tiers, regions, and privacy compliance and each one is a potential failure point. @@ -12,27 +26,23 @@ The solution? **LangGraph multi-agent workflows** controlled by **LaunchDarkly A In the next 18 minutes, you'll transform your basic multi-agent system with: -- **Business Tiers & MCP Integration**: Free users get internal RAG search, Paid users get premium models with external research tools and expanded tool call limits, all controlled by [LaunchDarkly AI Configs](https://launchdarkly.com/docs/home/ai-configs) +- **Business Tiers & MCP Integration**: Free users get internal keyword search, Paid users get premium models with RAG, external research tools and expanded tool call limits, all controlled by [LaunchDarkly AI Configs](https://launchdarkly.com/docs/home/ai-configs) - **Geographic Targeting**: EU users automatically get Mistral and Claude models (enhanced privacy), other users get cost-optimized alternatives -- **Smart Configuration**: Set up complex targeting matrices with [LaunchDarkly segments](https://launchdarkly.com/docs/home/flags/segments) and [targeting rules](https://launchdarkly.com/docs/home/flags/target-rules) +- **Smart Configuration**: Set up complex targeting matrices with [LaunchDarkly segments](https://launchdarkly.com/docs/home/flags/segments) and [targeting rules](https://launchdarkly.com/docs/home/flags/target) ## Prerequisites -> **⚠️ CRITICAL: Naming Requirements** -> -> The bootstrap script depends on exact naming from Part 1. You **MUST** have used these names: -> - **Project**: `multi-agent-chatbot` -> - **AI Configs**: `supervisor-agent`, `security-agent`, `support-agent` -> - **Tools**: `search_v2`, `reranking` -> - **Variations**: `supervisor-basic`, `pii-detector`, `rag-search-enhanced` -> -> If you used different names in Part 1, you'll need to either rename your resources or create new ones with the correct names before proceeding. - -You'll need: -- **Completed [Part 1**: Working multi-agent system with basic AI Configs](README.md) -- **Same environment**: Python 3.9+, uv, API keys from [Part 1](README.md) -- **LaunchDarkly API key**: Add `LD_API_KEY=your-api-key` to your `.env` file ([get API key](https://launchdarkly.com/docs/home/account/api)) -- **Mistral API key**: Add `MISTRAL_API_KEY=your-mistral-key` to your `.env` file ([get API key](https://console.mistral.ai/)) - required for EU privacy features +✅ **[Part 1 completed](README.md)** with exact naming: +- Project: `multi-agent-chatbot` +- AI Configs: `supervisor-agent`, `security-agent`, `support-agent` +- Tools: `search_v2`, `reranking` +- Variations: `supervisor-basic`, `pii-detector`, `rag-search-enhanced` + +🔑 **Add to your `.env` file**: +```bash +LD_API_KEY=your-api-key # Get from LaunchDarkly settings +MISTRAL_API_KEY=your-key # Get from console.mistral.ai (free, requires phone + email validation) +``` ### Getting Your LaunchDarkly API Key @@ -40,18 +50,39 @@ The automation scripts in this tutorial use the LaunchDarkly REST API to program To get your LaunchDarkly API key, start by navigating to Organization Settings by clicking the gear icon (⚙️) in the left sidebar of [your LaunchDarkly dashboard](https://app.launchdarkly.com/). Once there, access Authorization Settings by clicking **"Authorization"** in the settings menu. Next, create a new access token by clicking **"Create token"** in the "Access tokens" section. +
+ +
+ + ![API Token Creation](screenshots/api_token.png) -*Click "Create token" in the Access tokens section* + + +
When configuring your token, give it a descriptive name like "multi-agent-chatbot", select **"Writer"** as the role (required for creating configurations), use the default API version (latest), and leave "This is a service token" unchecked for now. +
+ +
+ + ![Name API Token](screenshots/name_api_token.png) -*Configure your token with a descriptive name and Writer role* + + +
After configuring the settings, click **"Save token"** and immediately copy the token value. This is **IMPORTANT** because it's only shown once! +
+ +
+ + ![Copy API Token](screenshots/copy_api_token.png) -*Copy the token value immediately - it's only shown once* + + +
Finally, add the token to your environment: ```bash @@ -65,7 +96,7 @@ Finally, add the token to your environment: Your agents need more than just your internal documents. **Model Context Protocol (MCP)** connects AI assistants to live external data and they agents become orchestrators of your digital infrastructure, tapping into databases, communication tools, development platforms, and any system that matters to your business. MCP tools run as separate servers that your agents call when needed. -The [MCP Registry](https://registry.modelcontextprotocol.io) serves as a community-driven directory for discovering available MCP servers - like an app store for MCP tools. For this tutorial, we'll use manual installation since our specific academic research servers (ArXiv and Semantic Scholar) aren't yet available in the registry. +> The [MCP Registry](https://registry.modelcontextprotocol.io) serves as a community-driven directory for discovering available MCP servers - like an app store for MCP tools. For this tutorial, we'll use manual installation since our specific academic research servers (ArXiv and Semantic Scholar) aren't yet available in the registry. Install external research capabilities: @@ -73,7 +104,7 @@ Install external research capabilities: # Install ArXiv MCP server for academic paper search uv tool install arxiv-mcp-server -# Install Semantic Scholar MCP server for citation data +# Install Semantic Scholar MCP server for citation data git clone https://github.com/JackKuo666/semanticscholar-MCP-Server.git ``` @@ -81,13 +112,11 @@ git clone https://github.com/JackKuo666/semanticscholar-MCP-Server.git - **arxiv_search**: Live academic paper search (Paid users) - **semantic_scholar**: Citation and research database (Paid users) -These tools integrate with your agents via LangGraph - LaunchDarkly controls which users get access to which tools. +These tools integrate with your agents via LangGraph while LaunchDarkly controls which users get access to which tools. ## Step 2: Configure with API Automation (2 minutes) -Now we'll use programmatic API automation to configure the complete setup including tools and targeting matrix. The [LaunchDarkly REST API](https://launchdarkly.com/docs/guides/api/rest-api) lets you manage tools, segments, and [AI Configs](https://launchdarkly.com/docs/home/ai-configs) programmatically. Instead of manually creating dozens of variations in the UI, you'll set up complex targeting matrices with a single Python script. This approach is essential when you need to handle multiple geographic regions × business tiers with conditional tool assignments. - -**What This Script Does**: This is **configuration automation**, not application deployment. The script makes REST API calls to LaunchDarkly to provision user segments, AI config variations, targeting rules, and tools - the same resources you could create manually through the LaunchDarkly dashboard. Your actual chat application continues running unchanged. +Now we'll use programmatic API automation to configure the complete setup. The [LaunchDarkly REST API](https://launchdarkly.com/docs/guides/api/rest-api) lets you manage tools, segments, and [AI Configs](https://launchdarkly.com/docs/home/ai-configs) programmatically. Instead of manually creating dozens of variations in the UI, this **configuration automation** makes REST API calls to provision user segments, AI Config variations, targeting rules, and tools. These are the same resources you could create manually through the LaunchDarkly dashboard. Your actual chat application continues running unchanged. Configure your complete targeting matrix with one command: @@ -96,88 +125,279 @@ cd bootstrap uv run python create_configs.py ``` -The configuration script intelligently handles existing resources from Part 1: -- **Reuses**: `supervisor-agent` (identical), existing `search_v2` and `reranking` tools -- **Updates**: `security-agent` with additional geographic compliance config variations -- **Creates New**: `support-agent` config variations for business tier targeting, plus new tools (`search_v1`, `arxiv_search`, `semantic_scholar`) - -**LaunchDarkly Resources Added** +**What the script creates**: - **3 new tools**: `search_v1` (basic search), `arxiv_search` and `semantic_scholar` (MCP research tools) - **4 combined user segments** with [geographic and tier targeting rules](https://launchdarkly.com/docs/home/flags/segments) -- **3 [AI Configs](https://launchdarkly.com/docs/home/ai-configs) Variations** with intelligent handling: - - **security-agent**: Updated with 2 new geographic variations (basic vs strict GDPR) - - **support-agent-business-tiers**: New config with 5 variations (geographic × tier matrix) -- **Complete [targeting rules](https://launchdarkly.com/docs/home/flags/target-rules)** that route users to appropriate variations +- **Updated AI Configs**: `security-agent` with 2 new geographic variations +- **Complete [targeting rules](https://launchdarkly.com/docs/home/flags/target)** that route users to appropriate variations +- **Intelligently reuses** existing resources: `supervisor-agent`, `search_v2`, and `reranking` tools from Part 1 + +### Understanding the Bootstrap Script + +The automation works by reading a YAML manifest and translating it into LaunchDarkly API calls. Here's how the key parts work: + +**Segment Creation with Geographic Rules**: +```python +def create_segment(self, project_key, segment_data): + # Step 1: Create empty segment + payload = { + "key": segment_data["key"], + "name": segment_data["key"].replace("-", " ").title() + } + + # Step 2: Add targeting rules via semantic patch + clauses = [] + for clause in segment_data["rules"]: + clauses.append({ + "attribute": clause["attribute"], # "country" or "plan" + "op": clause["op"], # "in" + "values": clause["values"], # ["DE", "FR", ...] or ["free"] + "contextKind": "user", + "negate": clause["negate"] # false for EU, true for non-EU + }) +``` + +**Model Configuration Mapping**: +```python +# The script maps your YAML model IDs to LaunchDarkly's internal keys +model_config_key_map = { + "claude-sonnet-4-6": "Anthropic.claude-sonnet-4-6", + "claude-haiku-4-5-20251001": "Anthropic.claude-haiku-4-5-20251001", + "gpt-4o": "OpenAI.gpt-4o", + "gpt-4o-mini": "OpenAI.gpt-4o-mini-2024-07-18", + "mistral-small-latest": "Mistral.mistral-small-latest" +} +``` + +**Customizing for Your Use Case**: + +To adapt this for your own multi-agent system: + +1. **Add your geographic regions** in the YAML segments: + ```yaml + - key: apac-paid + rules: + - attribute: "country" + values: ["JP", "AU", "SG", "KR"] # Your APAC countries + ``` + +2. **Define your business tiers**: + ```yaml + - attribute: "plan" + values: ["enterprise", "professional", "starter"] # Your pricing tiers + ``` + +3. **Map your models** in the script: + ```python + "your-model-id": "Provider.your-launchdarkly-key" + ``` + +The script handles the complexity of LaunchDarkly's API while letting you define your targeting logic in simple YAML. + +### Validating the Bootstrap Script + +**Expected terminal output:** +```bash +🚀 LaunchDarkly AI Config Bootstrap +================================================== +⚠️ IMPORTANT: This script is for INITIAL SETUP ONLY +📝 After bootstrap completes: + • Make ALL configuration changes in LaunchDarkly UI + • Do NOT modify ai_config_manifest.yaml + • LaunchDarkly is your single source of truth +================================================== + +🚀 Starting multi-agent system bootstrap (add-only)... +📦 Project: multi-agent-chatbot + +🔧 Creating tools... + ✅ Tool 'search_v1' created + ✅ Tool 'arxiv_search' created + ✅ Tool 'semantic_scholar' created + +🤖 Ensuring AI configs exist... +✅ AI Config 'supervisor-agent' exists +✅ AI Config 'security-agent' exists +✅ AI Config 'support-agent' exists + +🧩 Creating variations... + ✅ Variation 'strict-security' created + ✅ Variation 'eu-free' created + ✅ Variation 'eu-paid' created + ✅ Variation 'other-free' created + ✅ Variation 'other-paid' created + +📦 Creating segments (for targeting rules)... +✅ Empty segment 'eu-free' created + ✅ Rules added to segment 'eu-free' (final count: 1) +✅ Empty segment 'eu-paid' created + ✅ Rules added to segment 'eu-paid' (final count: 1) +✅ Empty segment 'other-free' created + ✅ Rules added to segment 'other-free' (final count: 1) +✅ Empty segment 'other-paid' created + ✅ Rules added to segment 'other-paid' (final count: 1) + +🎯 Updating targeting rules... +✅ Targeting rules updated for 'security-agent' +✅ Targeting rules updated for 'support-agent' + +✨ Bootstrap complete! +``` + +**In your LaunchDarkly dashboard**, navigate to your `multi-agent-chatbot` project. You should see: + +1. **AI Configs tab**: Three configs (`supervisor-agent`, `security-agent`, `support-agent`) with new variations +2. **Segments tab**: Four new segments (`eu-free`, `eu-paid`, `other-free`, `other-paid`) +3. **Tools tab**: Five tools total (including `search_v1`, `arxiv_search`, `semantic_scholar`) + +**Troubleshooting Common Issues**: + +❌ **Error: "LD_API_KEY environment variable not set"** +- Check your `.env` file contains: `LD_API_KEY=your-api-key` +- Verify the API key has "Writer" permissions in LaunchDarkly settings + +❌ **Error: "AI Config 'security-agent' not found"** +- Ensure you completed [Part 1](README.md) with exact naming requirements +- Verify your project is named `multi-agent-chatbot` +- Check that `supervisor-agent`, `security-agent`, and `support-agent` exist in your LaunchDarkly project + +❌ **Error: "Failed to create segment"** +- Your LaunchDarkly account needs segment creation permissions +- Try running the script again; it's designed to handle partial failures + +❌ **Script runs but no changes appear** +- Wait 30-60 seconds for LaunchDarkly UI to refresh +- Check you're looking at the correct project and environment (Production) +- Verify your API key matches your LaunchDarkly organization ## Step 3: See How Smart Segmentation Works (2 minutes) -Here's how it works: EU free users get Claude Haiku with basic search (privacy + cost efficiency). EU paid users get Claude Sonnet with full research tools (privacy + premium features). Non-EU free users get GPT-4o Mini with basic search (maximum cost efficiency). Non-EU paid users get GPT-4 with complete research tools (maximum capability). +Here's how the smart segmentation works: + +**By Region:** +- **EU users**: Mistral for security processing + Claude for support (privacy + compliance) +- **Non-EU users**: Claude for security + GPT for support (cost optimization) +- **All users**: Claude for supervision and workflow orchestration -This segmentation strategy optimizes costs through efficient models for free users while providing premium capabilities to paid users. It also enhances privacy by giving EU users Mistral models with a privacy-by-design approach. +**By Business Tier:** +- **Free users**: Basic search tools (`search_v1`) +- **Paid users**: Full research capabilities (`search_v1`, `search_v2`, `reranking`, `arxiv_search`, `semantic_scholar`) ## Step 4: Test Segmentation with Script (2 minutes) -The included test script simulates real user scenarios across all segments, verifying that your targeting rules work correctly. It sends actual API requests to your system and confirms each user type gets the right model, tools, and behavior - giving you confidence before real users arrive. +The included test script simulates real user scenarios across all segments, verifying that your targeting rules work correctly. It sends actual API requests to your system and confirms each user type gets the right model, tools, and behavior. -Validate your segmentation with the test script: +First, start your system: ```bash +# Terminal 1: Start the backend +uv run uvicorn api.main:app --reload --port 8000 + +# Terminal 2: Run the test script uv run python api/segmentation_test.py ``` +**Expected test output:** +```bash +🚀 COMPREHENSIVE TUTORIAL 2 SEGMENTATION TESTS +Testing Geographic + Business Tier Targeting Matrix +====================================================================== + +🔄 Running: EU Paid → Claude Sonnet + Full MCP Tools + +============================================================ +🧪 TESTING: DE paid user (ID: user_eu_paid_001) +============================================================ +📊 SUPPORT AGENT: + Model: claude-sonnet-4-6 (expected: claude-sonnet-4-6) ✅ + Variation: eu-paid (expected: eu-paid) ✅ + Tools: ['search_v1', 'search_v2', 'reranking', 'arxiv_search', 'semantic_scholar'] ✅ + Expected: ['search_v1', 'search_v2', 'reranking', 'arxiv_search', 'semantic_scholar'] + MCP Tools: Yes (should be: Yes) ✅ + +📝 RESPONSE: + Length: 847 chars + Tools Called: ['search_v2', 'arxiv_search'] + Preview: Based on your request, I'll search both internal documentation and recent academic research... + +🎯 RESULT: ✅ PASSED + +🔄 Running: EU Free → Claude Haiku + Basic Tools +[Similar detailed output for EU Free user...] + +🔄 Running: US Paid → GPT-4 + Full MCP Tools +[Similar detailed output for US Paid user...] + +🔄 Running: US Free → GPT-4o Mini + Basic Tools +[Similar detailed output for US Free user...] + +====================================================================== +📊 FINAL RESULTS +====================================================================== +✅ PASSED: 4/4 +❌ FAILED: 0/4 + +🎉 ALL TESTS PASSED! LaunchDarkly targeting is working correctly. + • Geographic segmentation: Working + • Business tier routing: Working + • Model assignment: Working + • Tool configuration: Working + • MCP integration: Working + +🔗 Next: Test manually in UI at http://localhost:8501 +``` + This confirms your targeting matrix is working correctly across all user segments! ## Step 5: Experience Segmentation in the Chat UI (3 minutes) -Now let's see your segmentation in action through the actual user interface that your customers will experience. +Now let's see your segmentation in action through the user interface. With your backend already running from Step 4, start the UI: ```bash -# Start your system (2 terminals) -uv run uvicorn api.main:app --reload --port 8001 -API_PORT=8001 uv run streamlit run ui/chat_interface.py --server.port 8501 +# Terminal 3: Start the chat interface +uv run streamlit run ui/chat_interface.py --server.port 8501 ``` Open http://localhost:8501 and test different user types: -1. **User Dropdown**: Select different regions (eu, other) and plans (Free, Paid) -2. **Ask Questions**: Try "Search for machine learning papers" -3. **Watch Workflow**: See which model and tools get used for each user type -4. **Verify Routing**: EU users get Mistral, Other users get GPT, Paid users get MCP tools +1. **User Dropdown**: Find the user dropdown by using the **>> icon** to open the **left nav menu**.. Select different regions (eu, other) and plans (Free, Paid). +2. **Ask Questions**: Try "Search for machine learning papers." +3. **Watch Workflow**: In the server logs, watch which model and tools get used for each user type. +4. **Verify Routing**: EU users get Mistral for security. Other users get GPT. Paid users get MCP tools. + ![Chat Interface User Selection](screenshots/chat_interface.png) -*Select different user types to test segmentation in the chat interface* - -## What You've Accomplished - -You've built a sophisticated multi-agent system that demonstrates how modern AI applications can handle complex user segmentation and feature differentiation. Automated configuration setup shows a practical approach to managing multi-dimensional targeting and provides a clear framework for expanding into additional geographic regions or business tiers as needed. - -Your multi-agent system now has: -- **Smart Geographic Routing**: Enhanced privacy protection for EU users -- **Business Tier Management**: Feature scaling that grows with customer value -- **API Automation**: Complex configurations created programmatically via [LaunchDarkly REST API](https://launchdarkly.com/docs/guides/api/rest-api) -- **External Tool Integration**: Research capabilities for premium users + ## What's Next: Part 3 Preview **In Part 3**, we'll prove what actually works using controlled A/B experiments: -### **Three-Experiment Strategy** +### **Set up Easy Experiments** - **Tool Implementation Test**: Compare search_v1 vs search_v2 on identical models to measure search quality impact -- **Model Efficiency Analysis**: Test Claude vs GPT-4 with the same full tool stack to measure tool-calling precision and cost -- **Security Configuration Study**: Compare basic vs strict security settings to quantify enhanced privacy costs +- **Model Efficiency Analysis**: Test models with the same full tool stack to measure tool-calling precision and cost ### **Real Metrics You'll Track** -- **User satisfaction** - thumbs up/down feedback -- **Tool call efficiency** - average number of tools used per successful query -- **Token cost analysis** - cost per query across different model configurations -- **Response latency** - performance impact of security and tool variations +- **User satisfaction**: thumbs up/down feedback +- **Tool call efficiency**: average number of tools used per successful query +- **Token cost analysis**: cost per query across different model configurations +- **Response latency**: performance impact of security and tool variations Instead of guessing which configurations work better, you'll have data proving which tool implementations provide value, which models use tools more efficiently, and what security enhancements actually costs in performance. -## Related Resources +## The Path Forward -Explore the **[LaunchDarkly MCP Server](https://launchdarkly.com/docs/home/getting-started/mcp)** - enable AI agents to access feature flag configurations, user segments, and experimentation data directly through the Model Context Protocol. +You've built something powerful: a multi-agent system that adapts to users by design. More importantly, you've proven that sophisticated AI applications don't require repeated deployments; they require smart configuration. ---- +This approach scales beyond tutorials. Whether you're serving 100 users or 100,000, the same targeting principles apply: segment intelligently, configure dynamically, and let data guide decisions instead of assumptions. -*Ready for data-driven optimization? Part 3 will show you how to run experiments that prove ROI and guide product decisions with real user behavior data.* \ No newline at end of file +## Related tutorials + +- [Build a LangGraph Multi-Agent system in 20 Minutes](https://launchdarkly.com/docs/tutorials/agents-langgraph) - Part 1: the multi-agent system this tutorial layers targeting on top of +- [Proving ROI with data-driven AI agent experiments](https://launchdarkly.com/docs/guides/experimentation/ai-experiments-roi) - Part 3: A/B test the targeted variations you just built +- [Beyond n8n for Workflow Automation: Agent Graphs](https://launchdarkly.com/docs/tutorials/agent-graphs) - Combine targeting with visual graph topology and per-node monitoring +- [Build AI Configs with Agent Skills](https://launchdarkly.com/docs/tutorials/agent-skills-quickstart) - Generate the agent and targeting configurations from natural-language prompts +- [Offline Evaluation of RAG-Grounded Answers](https://launchdarkly.com/docs/tutorials/offline-evals) - Validate each targeted variation against a reference dataset before rollout + +--- +*Questions? Issues? Reach out at `aiproduct@launchdarkly.com` or open an issue in the [GitHub repo](https://github.com/launchdarkly-labs/devrel-agents-tutorial/tree/tutorial/agent-graphs).* diff --git a/tutorial_3.md b/tutorial_3.md index 855b014..8f9d6fb 100644 --- a/tutorial_3.md +++ b/tutorial_3.md @@ -1,325 +1,351 @@ -# Proving ROI with Data-Driven AI Agent Experiments +# Proving ROI with data-driven AI agent experiments -
-From Guessing to Knowing - Prove ROI with Data-Driven AI Experiments -
+> Read the published version on [LaunchDarkly Docs](https://launchdarkly.com/docs/guides/experimentation/ai-experiments-roi). -
+## Overview -## What You'll Learn in 5 Minutes (or Build in 30) + -> **Key Findings from Our Experiments:** -> - **Strict security is a win**: 36% faster response times + enhanced privacy for only 14.6% cost increase -> - **Unexpected discovery**: Free Mistral model is not only $0 but also significantly faster than Claude Haiku -> - **Cost paradox revealed**: "Free" security agent increased total system costs by forcing downstream agents to compensate -> - **Premium model failure**: Claude Opus 4 performed 64% worse than GPT-4o despite costing 33% more -> - **Sample size reality**: High-variance metrics (cost, feedback) require 5-10x more data than low-variance ones (latency) +This guide was published in October 2025, before LaunchDarkly shipped several features that supersede or extend the experiment patterns shown below. The methodology still works, but for new experiments you'll likely also want to use: -## The Problem +- [**Online evaluations**](https://launchdarkly.com/docs/home/ai-configs/online-evaluations) and [**custom judges**](https://launchdarkly.com/docs/home/ai-configs/custom-judges) — LLM-as-a-judge scores feed directly into experiment metrics, so judge accuracy or toxicity can be the primary metric +- [**Agent graphs**](https://launchdarkly.com/docs/home/ai-configs/agent-graphs) — A/B test routing topology and per-node model choices, not just whole-config swaps +- [**Offline evaluations + datasets**](https://launchdarkly.com/docs/home/ai-configs/offline-evaluations) — lock in a regression baseline before running a live experiment +- [**LLM Playground**](https://launchdarkly.com/docs/home/ai-configs/playground) — pilot variations on test prompts before sending them to real traffic -Your CEO asks: **"Is the new expensive AI model worth it?"** +LaunchDarkly is also rebranding **AI Configs** as **AgentControl**. Slugs, SDK names, and APIs are unchanged. For the current reference, see [AgentControl documentation](https://launchdarkly.com/docs/home/ai-configs). -Your product manager worries: **"Will aggressive PII redaction hurt user satisfaction?"** + -Your finance team wonders: **"Does the enhanced privacy justify the cost?"** +This guide explains how to measure and prove the return on investment (ROI) of AI model changes with LaunchDarkly experiments. It shows you how to use statistical analysis to find unexpected performance wins and cost paradoxes. -Without experiments, you're guessing. This tutorial shows you how to **measure the truth** - and sometimes discover unexpected wins. +Imagine a scenario where your CEO asks you "Is the new expensive AI model worth it? And does the enhanced privacy justify the cost?" By the end of this guide, you will be able to answer this question. -## The Solution: Real Experiments, Real Answers +You will run two A/B tests that reveal: -In 30 minutes, you'll run actual A/B tests that reveal: +- Whether aggressive PII redaction hurts user satisfaction +- Whether Claude Opus 4 is worth 33% more than GPT-4o +- Any other unexpected gains from using the new model -**Does strict security hurt positive feedback rates?** -**Is Claude Opus 4 worth 33% more than GPT-4o?** +You can use this guide in one of two ways: -*Part 3 of 3: **Chaos to Clarity: Defensible AI Systems That Deliver*** +- **Just the concepts** (5-minute read): Skip to [About the two experiments](#about-the-two-experiments) to learn the methodology without running code. +- **Full working tutorial** (30 minutes): Follow the complete guide to run your own experiments. -## Quick Start Options +## Prerequisites -### **Option 1: Just Want the Concepts?** (5 min read) -Skip to [Understanding the Experiments](#understanding-your-two-experiments) to learn the methodology without running code. +If you choose to complete the full tutorial, you need the following prerequisites: -### **Option 2: Full Hands-On Tutorial** (30 min) -Follow the complete guide to run your own experiments. +- An active LaunchDarkly project, set up according to the following tutorials: + - [Build a LangGraph Multi-Agent system in 20 Minutes with LaunchDarkly AI Configs](https://launchdarkly.com/docs/tutorials/agents-langgraph) + - [Smart AI Agent Targeting with MCP Tools](https://launchdarkly.com/docs/tutorials/multi-agent-mcp-targeting) +- API keys for Anthropic, OpenAI, and LaunchDarkly: + - [Sign up for a free LaunchDarkly account](http://app.launchdarkly.com/signup) and then [follow these instructions to get your API access token](https://launchdarkly.com/docs/home/account/api-create). -
-Prerequisites for Hands-On Tutorial +### Agent costs -**Required from Previous Parts:** -- Completed Parts [1](README.md) & [2](README.md) (multi-agent system with segmentation) -- Active LaunchDarkly project with AI Configs -- API keys: Anthropic, OpenAI, LaunchDarkly +The agent costs associated with the experiment may cost anywhere from \$5 to \$35. The default walk-through uses Claude Opus 4 (premium model) for testing. To reduce costs while still learning the experimentation patterns, you can modify `bootstrap/tutorial_3_experiment_variations.py` in your cloned repository to test with the free Mistral model instead. -**Investment:** -- Time: ~30 minutes (15 min setup, 10 min data collection, 5 min analysis) -- Cost: $25-35 default ($5-10 with `--queries 50`) +To reduce costs, change the following in the `create_premium_model_variations` function: -
+ + -## How the Experiments Work +```python +# Original (expensive): +"model": { + "name": "claude-opus-4-7", + "provider": "anthropic" +} -**The Setup**: Your AI system will automatically test variations on simulated users, collecting real performance data that flows directly to LaunchDarkly for statistical analysis. - -**The Process**: -1. **Traffic simulation** generates queries from your actual knowledge base -2. **Each user** gets randomly assigned to experiment variations -3. **AI responses** are evaluated for quality and tracked for cost/speed -4. **LaunchDarkly** calculates statistical significance automatically - -**Note**: The two experiments can run independently. Each user can participate in both, but the results are analyzed separately. - -**Reality Check**: Not all metrics reach significance at the same rate. In our security experiment with 2,727 users: -- **Latency**: 87% confidence (nearly significant, clear 36% improvement) -- **Cost**: 21% confidence (high variance, needs 5-10x more data) -- **Feedback**: 58% confidence (sparse signal, needs 5-10x more data) - -This is normal! Low-variance metrics (latency, tokens) prove out quickly. High-variance metrics (cost, feedback) need massive samples. **You may not be able to wait for every metric to hit 90%** - use strong signals on some metrics plus directional insights on others. - -**Experiment Methodology**: Our supervisor agent routes PII queries to the security agent (then to support), while clean queries go directly to support. LaunchDarkly tracks metrics **at the user level across all agents**, revealing system-wide effects. We ran 500+ PII queries (`--pii-percentage 100`) to maximize security agent traffic. Cost tracking captures all three agents (supervisor, security, support), showing how "free" security models can paradoxically increase total system costs. - -## Understanding Your Two Experiments - -> **Before:** You guess whether stricter security helps or hurts. -> **After:** You'll have mathematical proof of user preferences. - -### **Experiment 1: Security Agent Analysis** - -**Question**: Does Strict Security (free Mistral model with aggressive PII redaction) improve performance without harming user experience or significantly increasing system costs? - -**Variations** (50% each): -- **Control**: Basic Security (Claude Haiku, moderate PII redaction) -- **Treatment**: Strict Security (Mistral free, aggressive PII redaction) - -**Success Criteria**: -1. Positive feedback rate: stable or improving (not significantly worse) -2. Cost increase: ≤15% with ≥75% confidence -3. Latency increase: ≤3 seconds (don't significantly slow down) -4. Enhanced privacy protection delivered - -### **Experiment 2: Premium Model Value Analysis** - -**Question**: Does Claude Opus 4 justify its premium cost vs GPT-4o? - -**Variations** (50% each): -- **Control**: GPT-4o with full tools (current version) -- **Treatment**: Claude Opus 4 with identical tools - -**Success Criteria (must meet 90% threshold)**: -- ≥15% positive feedback rate improvement by Claude Opus 4 -- Cost-value ratio ≥ 0.25 (positive feedback rate gain % ÷ cost increase %) - -## Setting Up Metrics and Experiments - -> **Why this matters:** Without metrics, you're flying blind. These five metrics reveal the truth about AI performance. - -### **Step 1: Configure Metrics (5 minutes)** - -#### **Quick Metric Setup** - -Navigate to **Metrics** in LaunchDarkly and create three custom metrics: +# Change to (free Mistral): +"model": { + "name": "mistral-small-latest", + "provider": "mistral" +} +``` -| Metric | Event Key | Type | What It Measures | -|--------|-----------|------|------------------| -| **P95 Latency** | `$ld:ai:duration:total` | P95 | Response speed | -| **Avg Tokens** | `$ld:ai:tokens:total` | Average | Token usage | -| **Cost/Request** | `ai_cost_per_request` | Average | Dollar cost | -| **Positive Feedback** | Built-in | Rate | Positive feedback rate | -| **Negative Feedback** | Built-in | Rate | User complaints | + + + +This reduces the experiment cost by about \$20. You will still have costs from the control group using GPT-4o and other agents in the system. + +## How the experiments work + +Your AI system will automatically test variations on simulated users, collecting real performance data that flows directly to LaunchDarkly for statistical analysis. + +Here's how: + +- Traffic simulation generates queries from your knowledge base +- The experiments randomly assign each user to experiment variations +- LaunchDarkly evaluates AI responses for quality and tracks cost and speed +- LaunchDarkly calculates statistical significance automatically + +The two experiments can run independently. Users can participate in both, but the results are analyzed separately. + +### Methodology + +Our supervisor agent routes PII queries to the security agent, then to support, while clean queries go directly to support. LaunchDarkly tracks metrics at the user level across all agents, which can reveal system-wide effects. -
-See detailed setup for P95 Latency +## About the two experiments -1. Event key: `$ld:ai:duration:total` -2. Type: Value/Size → Numeric, Aggregation: Sum -3. Definition: P95, value, user, sum, "lower is better" -4. Unit: `ms`, Name: `p95_total_user_latency` +This guide explains how to run two concurrent experiments: -
-P95 Setup -
+- Security agent analysis +- Premium model value analysis -
+The experiments are explained below. -
-View other metric configurations +### Experiment 1: Security agent analysis -- **Tokens**: Same as latency but Average instead of P95 -- **Cost**: Event key `ai_cost_per_request`, Average in dollars -- Screenshots: `screenshots/tokens.png` and `screenshots/cost.png` +Here is an overview of the first experiment: -
+- Hypothesis: "Strict security using free Mistral model with aggressive PII redaction will improve performance without harming user experience or significantly increasing system costs." +- Variations (50% each): + - **Control**: Basic security with Claude Haiku, including moderate PII redaction + - **Treatment**: Strict security with Mistral free, including aggressive PII redaction +- Success criteria: + - Positive feedback rate: stable or improving (not significantly worse) + - Cost increase: ≤15% with ≥75% confidence + - Latency increase: ≤3 seconds (don't significantly slow down) + - Enhanced privacy protection delivered -The cost tracking is implemented in `utils/cost_calculator.py`, which calculates actual dollar costs using the formula `(input_tokens × input_price + output_tokens × output_price) / 1M`. The system has pre-configured pricing for each model: GPT-4o at $2.50/$10 per million tokens, Claude Opus 4 at $15/$75, and Claude Sonnet at $3/$15. When a request completes, the cost is immediately calculated and sent to LaunchDarkly as a custom event, enabling direct cost-per-user analysis in your experiments. +### Experiment 2: Premium model value analysis -### **Step 2: Create Experiment Variations** +Here is an overview of the second experiment: -Create the experiment variations using the bootstrap script: +- Hypothesis: "Claude Opus 4 will justify its premium cost over GPT-4o due to its improved feedback rate." +- Variations (50% each): + - **Control**: GPT-4o with full tools (current version) + - **Treatment**: Claude Opus 4 with identical tools +- Success criteria (must meet 90% threshold): + - ≥15% positive feedback rate improvement by Claude Opus 4 + - Cost-value ratio ≥ 0.25 (positive feedback rate gain % ÷ cost increase %) + +## Step 1: Set up metrics + +This section explains how to configure the metrics needed for your two experiments. + +Navigate to **Metrics** in LaunchDarkly and [create three custom metrics](https://launchdarkly.com/docs/home/metrics/create-metrics): + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Metric 1Metric 2Metric 3
Event key`$ld:ai:duration:total``$ld:ai:tokens:total``ai_cost_per_request`
MeasurementValue/sizeValue/sizeValue/size
AggregationSumSumSum
DefinitionP95, lower is betterAverage, lower is betterAverage, lower is better
Metric namep95_total_user_latencyaverage_total_user_tokensai_cost_per_request
What it measuresResponse speedToken usageDollar cost
+ +Expand the sections below to view the full metric setups. + + + +Here is the detailed setup for the P95 latency metric: + + + ![P95 Setup](screenshots/user_duration.png) + + + + + + +Here is the detailed setup for the total tokens metric: + + + ![Tokens](screenshots/tokens.png) + + + + + + +Here is the detailed setup for the cost per request metric: + + + ![Cost](screenshots/cost.png) + + + + +In addition to the above custom metrics, you will also use these [built-in metrics](https://launchdarkly.com/docs/home/metrics/autogen/ai): + + + + + + + + + + + + + + + + + + + + + + +
Event keyBuilt-inBuilt-in
NamePositive feedbackNegative feedback
TypeRateRate
What it measuresPositive feedback rateUser complaints
+ +The cost tracking is implemented in `utils/cost_calculator.py`, which calculates actual dollar costs using the formula `(input_tokens × input_price + output_tokens × output_price) / 1M`. The system has pre-configured pricing for each model as of October 2025: GPT-4o at \$2.50/\$10 per million tokens, Claude Opus 4 at \$15/\$75, and Claude Sonnet at \$3/\$15. When a request completes, the cost is immediately calculated and sent to LaunchDarkly as a custom event, enabling direct cost-per-user analysis in your experiments. + +## Step 2: Create experiment variations + +Create the experiment variations using this bootstrap script: + + + ```bash uv run python bootstrap/tutorial_3_experiment_variations.py ``` -This creates the `claude-opus-treatment` variation for the Premium Model Value experiment. The Security Agent Analysis experiment will use your existing baseline and enhanced variations. Both experiments use the existing other-paid configuration as their control group. - -### **Step 3: Configure Security Agent Experiment** - -
-Click for details - -Navigate to AI Configs → security-agent → Experiments tab → Create experiment -#### **Experiment Design** - -**Experiment type:** -- Keep `Feature change` selected (default) - -**Name:** `Security Level` - -#### **Hypothesis and Metrics** - -**Hypothesis:** `Enhanced security improves safety compliance without significantly harming positive feedback rates` - -**Randomize by:** `user` - -**Metrics:** Click "Select metrics or metric groups" and add: -1. `Positive feedback rate` → Select first to set as **Primary** -2. `Negative feedback rate` -3. `p95_total_user_latency` -4. `ai_cost_per_request` - -#### **Audience Targeting** - -**Flag or AI Config** -- Click the dropdown and select **security-agent** - -**Targeting rule:** -- Click the dropdown and select **Rule 4** -- This will configure: `If Context` → `is in Segment` → `Other Paid` - -#### **Audience Allocation** - -**Variations served outside of this experiment:** -- `Basic Security` - -**Sample size:** Set to `100%` of users in this experiment - -**Variations split:** Click "Edit" and configure: -- `pii-detector`: `0%` -- `Basic Security`: `50%` -- `Strict Security`: `50%` - -**Control:** -- `Basic Security` - -#### **Statistical Approach and Success Criteria** - -**Statistical approach:** `Bayesian` -**Threshold:** `90%` - -Click **"Save"** -Click **"Start experiment"** to launch. + + -
+This creates the `claude-opus-treatment` variation for the "Premium model value analysis" experiment. To verify the script worked correctly, navigate to your "support-model-config" feature flag in LaunchDarkly. You should now see the `claude-opus-treatment` variation alongside your existing variations. The "Security agent analysis" experiment will use your existing baseline and enhanced variations. Both experiments use the existing `other-paid` configuration as their control group. -
+## Step 3: Configure the security agent experiment -
-Security Agent Experiment Configuration -
+In the left navigation, expand **AI**, then select **Configs**, then open **security-agent**. In the right navigation, click the plus **+** sign next to **Experiments** to create a new experiment. -
+Here is how to set up your experiment: -### **Step 4: Configure Premium Model Experiment** +- **Name:** Security level +- **Hypothesis:** "Strict security using free Mistral model with aggressive PII redaction will improve performance without harming user experience or significantly increasing system costs." +- **Randomize by:** `user` +- **Metrics:** Click "Select metrics or metric groups" and add: + - `Positive feedback rate`: Select first to set as **Primary** + - `Negative feedback rate` + - `p95_total_user_latency` + - `ai_cost_per_request` +- **Flag or AI Config**: security-agent +**Targeting rule:** Rule 4 + - This configures `If Context` > `is in Segment` then `Other Paid` +- **Variations served outside of this experiment:** `Basic Security` +- **Sample size:** Set to `100%` of users in this experiment +- **Variations split:** Click "Edit" and configure: + - `pii-detector`: `0%` + - `Basic Security`: `50%` + - `Strict Security`: `50%` + - To set these percentages, first scroll down to the **Control** field and set `Basic Security` as the control variation, otherwise you won't be able to allocate 50% traffic to it. +- **Control:** `Basic Security` +- **Statistical approach:** `Bayesian` +- **Threshold:** `90%` -
-Click for details +Click **Save**, then click **Start experiment**. -Navigate to AI Configs → support-agent → Experiments tab → Create experiment +You may see a "Health warning" indicator after starting the experiment. This is normal and expected when no variations have been exposed yet. The warning will clear after your experiment starts receiving traffic and data begins flowing. -#### **Experiment Design** + + ![Security agent experiment configuration](screenshots/security_level.png) + -**Experiment type:** -- Keep `Feature change` selected (default) +## Step 4: Configure the premium model experiment -**Name:** `Premium Model Value Analysis` +In the left navigation, expand **AI**, then select **Configs**, then open **support-agent**. In the right navigation, click the plus **+** sign next to **Experiments** to create a new experiment. -#### **Hypothesis and Metrics** +Here is how to set up your experiment: -**Hypothesis:** `Claude Opus 4 justifies premium cost with superior positive feedback rate` +- **Name:** Premium model value analysis +- **Hypothesis:** "Claude Opus 4 will justify its premium cost over GPT-4o due to its improved feedback rate." +- **Randomize by:** `user` +- **Metrics:** Click "Select metrics or metric groups" and add: + - `Positive feedback rate`: Select first to set as **Primary** + - `Negative feedback rate` + - `p95_total_user_latency` + - `average_total_user_tokens` + - `ai_cost_per_request` +- **Flag or AI Config**: support-agent +- **Targeting rule:** Rule 4 + - This configures `If Context` > `is in Segment` then `Other Paid` +- **Variations served outside of this experiment:** `other-paid` +- **Sample size:** Set to `100%` of users in this experiment +- **Variations split:** Click "Edit" and configure: + - `rag-search-enhanced`: `0%` + - `eu-free`: `0%` + - `eu-paid`: `0%` + - `other-free`: `0%` + - `other-paid`: `50%` + - `international-standard`: `0%` + - `claude-opus-treatment`: `50%` + - To set these percentages, first scroll down to the **Control** field and set `Basic Security` as the control variation, otherwise you won't be able to allocate 50% traffic to it. +- **Control:** `other-paid` +- **Statistical approach:** `Bayesian` +- **Threshold:** `90%` -**Randomize by:** `user` +Click **Save**, then click **Start experiment**. -**Metrics:** Click "Select metrics or metric groups" and add: -1. `Positive feedback rate` → Select first to set as **Primary** -2. `Negative feedback rate` -3. `p95_total_user_latency` -4. `average_total_user_tokens` -5. `ai_cost_per_request` +You may see a "Health warning" indicator after starting the experiment. This is normal and expected when no variations have been exposed yet. The warning will clear once your experiment starts receiving traffic and data begins flowing. -#### **Audience Targeting** + + ![Premium model value analysis experiment configuration.](screenshots/premium_model.png) + -**Flag or AI Config** -- Click the dropdown and select **support-agent** +### Understanding your experiment design -**Targeting rule:** -- Click the dropdown and select **Rule 4** -- This will configure: `If Context` → `is in Segment` → `Other Paid` +If two independent experiments are running concurrently on the same users, then each user experiences: -#### **Audience Allocation** +- One security variation (`Basic Security` or `Strict Security`) +- One model variation (`Claude Opus 4 Treatment` or `Other Paid (GPT-4o)`) -**Variations served outside of this experiment:** -- `other-paid` +Random assignment ensures balance, because ~50% of users receive each combination. -**Sample size:** Set to `100%` of users in this experiment +## Step 5: Run a traffic generator -**Variations split:** Click "Edit" and configure: -- `rag-search-enhanced`: `0%` -- `eu-free`: `0%` -- `eu-paid`: `0%` -- `other-free`: `0%` -- `other-paid`: `50%` -- `international-standard`: `0%` -- `claude-opus-treatment`: `50%` +Start your backend and generate realistic experiment data. Choose between sequential or concurrent traffic generation. -**Control:** -- `other-paid` +### Concurrent traffic generator -#### **Statistical Approach and Success Criteria** +We recommend this option for large datasets. -**Statistical approach:** `Bayesian` -**Threshold:** `90%` +Here is the code for faster experiment data generation with parallel requests: -Click **"Save"** -Click **"Start experiment"** to launch. - -
- -
- -
-Premium Model Value Analysis Experiment Configuration -
- -
- -## Understanding Your Experimental Design - -**If Two Independent Experiments Running Concurrently:** - -Since these are the **same users**, each user experiences: -- One security variation (baseline OR enhanced) -- One model variation (GPT-4o OR Opus 4) - -Random assignment ensures balance: ~50 users get each combination naturally. - -## Generating Experiment Data - -### **Step 5: Run Traffic Generator** - -Start your backend and generate realistic experiment data. Choose between sequential or concurrent traffic generation: - -#### **Concurrent Traffic Generator (Recommended for large datasets)** - -For faster experiment data generation with parallel requests: + + ```bash # Start backend API @@ -329,44 +355,47 @@ uv run uvicorn api.main:app --reload --port 8000 uv run python -u tools/concurrent_traffic_generator.py --queries 200 --concurrency 10 ``` -**Configuration**: -- **200 queries** by default (edit script to adjust) -- **10 concurrent requests** running in parallel -- **2000-second timeout** (33 minutes) per request to handle MCP tool rate limits -- **Logs saved** to `logs/concurrent_experiment_TIMESTAMP.log` + + + +This configuration includes: -Note: runtime depends largely on if you retain MCP tool enablement as these take much longer to complete. +- 200 queries by default. You can edit the script to change this number. +- 10 concurrent requests running in parallel. +- 2000-second timeout (33 minutes) per request to handle MCP tool rate limits. -
-For smaller test runs or debugging +Runtime depends largely on if you retain MCP tool enablement as these take much longer to complete. -#### **Sequential Traffic Generator (Simple, one-at-a-time)** +### Sequential traffic generator + +We recommend this option for simple, one-at-a-time traffic generation: + + + ```bash # Start backend API uv run uvicorn api.main:app --reload --port 8000 # Generate experiment data sequentially (separate terminal) -python tools/traffic_generator.py --queries 50 --delay 2 +uv run python tools/traffic_generator.py --queries 50 --delay 2 ``` -**What Happens During Simulation:** - -1. **Knowledge extraction** - Claude analyzes your docs and identifies 20+ realistic topics + + -2. **Query generation** - Each test randomly selects from these topics for diversity +What happens during traffic simulation: -3. **AI-powered evaluation** - Claude judges responses as thumbs_up/thumbs_down/neutral +1. **Knowledge extraction:** Claude analyzes your docs and identifies 20+ realistic topics +2. **Query generation:** Each test randomly selects from these topics for diversity +3. **AI-powered evaluation:** Claude judges responses as thumbs_up/thumbs_down/neutral +4. **Automatic tracking:** All metrics flow to LaunchDarkly in real-time -4. **Automatic tracking** - All metrics flow to LaunchDarkly in real-time +Here is an example of the traffic generation output you might see: -
+ + -**Generation Output**: ``` 📚 Analyzing knowledge base... ✅ Generated 23 topics @@ -388,60 +417,70 @@ Failed: 5/200 (2.5%) Average: 13.6s per query (with concurrency) ``` -**Performance Notes**: + + + +Relevant performance information includes: + - Most queries complete in 10-60 seconds - Queries using `semantic_scholar` MCP tool may take 5-20 minutes due to API rate limits - Concurrent execution handles slow requests gracefully by continuing with others -- Failed/timeout requests (<5% typically) don't affect experiment validity +- Failed/timeout requests (typically less than 5%) don't affect experiment validity + +### Monitor results + +Refresh your LaunchDarkly experiment **Results** tabs to see data flowing in. Cost metrics appear as custom events alongside feedback and token metrics. + +## Interpreting your results -**Monitor Results**: Refresh your LaunchDarkly experiment "Results" tabs to see data flowing in. Cost metrics appear as custom events alongside feedback and token metrics. +After your experiments have collected data from about 100 users per variation, you'll see results in the LaunchDarkly UI. Here's how to interpret them: -## Interpreting Your Results (After Data Collection) +### Question 1: Security agent analysis -Once your experiments have collected data from ~100 users per variation, you'll see results in the LaunchDarkly UI. Here's how to interpret them: +Question: Does enhanced security improve safety without significantly impacting positive feedback rates? -### **1. Security Agent Analysis: Does enhanced security improve safety without significantly impacting positive feedback rates?** +Answer: Not all metrics reach significance at the same rate. In our security experiment we ran over 2,000 more users than the model experiment, turning off the MCP tools and using `--pii-percentage 100` to maximize PII detection. -> ## ✅ VERDICT: Deploy Strict Security: Enhanced Privacy Worth the Modest Cost -> -> **Sample:** 2,727 users (1,350 Basic Security, 1,377 Strict Security) +This is what we found: -**Latency (p95) - APPROACHING SIGNIFICANCE (Unexpected Win!)** -- **87.39% confidence** that Strict Security is faster -- **36% improvement**: 38.1ms (Basic) → 24.4ms (Strict) -- **Result**: Surprising performance advantage; Mistral not only costs $0 but is also significantly faster than Claude Haiku +- **Latency**: 87% confidence (nearly significant, clear 36% improvement) +- **Cost**: 21% confidence (high variance, needs 5-10x more data) +- **Feedback**: 58% confidence (sparse signal, needs 5-10x more data) + +This is normal. Low-variance metrics (latency, tokens) produce results quickly. High-variance metrics (cost, feedback) need massive samples. You may not be able to wait for every metric to hit 90%. Some metrics produce strong signals quickly, but for others, draw conclusions from the data and judgment based on your own experience. + +#### Verdict: Deploy strict security + +These results show that enhanced privacy is worth the modest cost. -**Cost Per Request - APPROACHING SIGNIFICANCE** -- **79.12% confidence** that Basic Security costs less (or 20.88% that Strict costs more) -- **+14.58% increase**: $0.0246 (Basic) → $0.0281 (Strict) -- **Result**: Modest cost increase with reasonable confidence - the enhanced privacy protection of Strict Security costs only ~$0.0035 more per request +The results tell a compelling story: -**Positive Feedback Rate - INCONCLUSIVE** -- **58.38% confidence** that Strict Security is better -- **+1.14% improvement**: 33.95% (Basic) → 34.34% (Strict) -- **Result**: Slight positive trend, needs more data +- Latency (p95) is approaching significance with 87% confidence that "Strict security" is faster, a win we didn't anticipate. +- Cost per request shows 79% confidence that "Basic security" costs less, or conversely, 21% confidence that "Strict" costs more, also approaching significance. +- Positive feedback rate remains inconclusive with only 58% confidence that "Strict security" performs better, indicating we need more data to draw conclusions about user satisfaction. -**The Hidden Cost Paradox:** +#### The hidden cost paradox -Strict Security uses **FREE Mistral** for PII detection, yet **increases total system cost by 14.6%**. Why? +Strict security uses free Mistral for PII detection, yet increases total system cost by 14.6%: ``` Basic Security (Claude Haiku): -- Supervisor: gpt-4o-mini ~$0.0001 -- Security: claude-haiku ~$0.0003 -- Support: gpt-4o ~$0.0242 -Total: $0.0246 +- Supervisor: gpt-4o-mini ~\$0.0001 +- Security: claude-haiku ~\$0.0003 +- Support: gpt-4o ~\$0.0242 +Total: \$0.0246 Strict Security (Mistral): -- Supervisor: gpt-4o-mini ~$0.0001 -- Security: mistral $0.0000 (FREE!) -- Support: gpt-4o ~$0.0280 (+15.7%) -Total: $0.0281 (+14.6%) +- Supervisor: gpt-4o-mini ~\$0.0001 +- Security: mistral \$0.0000 (FREE!) +- Support: gpt-4o ~\$0.0280 (+15.7%) +Total: \$0.0281 (+14.6%) ``` -**Why does the support agent cost more?** More aggressive PII redaction removes context, forcing the support agent to generate longer, more detailed responses to compensate for the missing information. This demonstrates why **system-level experiments** matter - optimizing one agent can inadvertently increase costs downstream. +Why does the support agent cost more? More aggressive PII redaction removes context, forcing the support agent to generate longer, more detailed responses to compensate for the missing information. This demonstrates why system-level experiments matter. Optimizing one agent can inadvertently increase costs downstream. + +Decision logic: -**Decision Logic:** ``` IF latency increase ≤ 3s AND cost increase ≤ 15% AND confidence ≥ 75% @@ -451,35 +490,30 @@ THEN deploy_strict_security() ELSE need_more_data() ``` -**Bottom line:** Deploy Strict Security. We expected latency to stay within 3 seconds of baseline, but discovered a **36% improvement** instead (87% confidence) - Mistral is significantly faster than Claude Haiku. Combined with enhanced privacy protection, this more than justifies the modest 14.58% cost increase (79% confidence). At scale, paying ~$0.0035 more per request for significantly better privacy compliance *and* faster responses is a clear win for EU users and privacy-conscious segments. - -**Key Insight:** The experiment revealed an unexpected performance advantage alongside the expected privacy benefits. While the "free" Mistral model does increase total system costs by forcing downstream agents to work harder with reduced context, the latency gains and privacy improvements make this a worthwhile tradeoff. LaunchDarkly's user-level tracking was essential for discovering both the cost increase and the performance win - neither would be visible in agent-level metrics alone. - +Conclusion: Deploy strict security. We expected latency to stay within 3 seconds of baseline, but discovered a 36% improvement instead (with 87% confidence). Mistral is significantly faster than Claude Haiku. Combined with enhanced privacy protection, this more than justifies the modest 14.5% cost increase (with 79% confidence). -**The Data That Proves It:** -
+At scale, paying ~\$0.004 more per request for significantly better privacy compliance *and* faster responses is a clear win for EU users and privacy-conscious segments. -
-Security Level Experiment Results -
+Here are the results: -
+ + ![Security level experiment results](screenshots/security_results.png) + -### **2. Premium Model Value Analysis: Does Claude Opus 4 justify its premium cost with superior positive feedback rates?** +### Question 2: Premium model value analysis -> ## 🔴 VERDICT: Reject Claude Opus 4 -> -**Performance:** 63% WORSE positive feedback rate (5.31% vs 14.55%) +Question: Does Claude Opus 4 justify its premium cost with superior positive feedback rates? -**Probability:** 99.52% that GPT-4o is superior +Answer: Reject Claude Opus 4. -**Cost:** 33% more expensive ($0.0159 vs $0.0119) +The experiment delivered a decisive verdict: -**Speed:** 81% slower (223ms vs 123ms) +- Positive feedback rate showed a significant failure with 99.5% confidence that GPT-4o is superior. +- Cost per request is approaching significance with 76% confidence that Claude Opus is 33% more expensive, while latency (p95) reached significance with 91% confidence that Claude Opus is **81% slower**. +- The cost-to-value ratio tells the whole story: -1.9x, meaning we're paying 33% more for 64% worse performance, which is a clear case of premium pricing without premium results. -**Cost-to-value Ratio:** -63%/33% = -.18 +Decision logic: -**Decision Logic:** ``` IF positive_feedback_rate increase ≥ 15% AND probability_to_beat for positive_feedback_rate ≥ 90% @@ -489,152 +523,152 @@ THEN deploy_claude_opus_4() ELSE keep_current_model() ``` -**Bottom line:** Premium price delivered worse results on every metric. Experiment was stopped when positive feedbarck rate reached significance. - -**Read across:** GPT-4o dominates on performance, and most likely also on speed, and cost - -**The Numbers Don't Lie:** +Conclusion: Premium price delivered worse results on every metric. Experiment was stopped when positive feedback rate reached significance. -
+GPT-4o dominates on performance and speed and most likely also on cost. -
-Premium Model Value Analysis Results -
+Here are the results: -
+ + ![Premium model value analysis results](screenshots/premium_results.png) + -### **Key Insights from Real Experiment Data** +### What we learned from our experiment data -**1. Metric Variance Determines Sample Size Requirements** +Here's what we learned running these experiments ourselves: -Low-variance metrics (latency, tokens) reach significance quickly (~1,000 samples). High-variance metrics (cost, feedback) may need 5,000-10,000+ samples. This isn't a flaw in your experiment but the reality of statistical power. Don't chase 90% confidence on every metric; focus on directional insights for high-variance metrics and statistical proof for low-variance ones. +- Low-variance metrics (latency, tokens) reach significance quickly (~1,000 samples). High-variance metrics (cost, feedback) may need 5,000-10,000+ samples. This isn't a flaw in your experiment but the reality of statistical power. Don't chase 90% confidence on every metric; focus on directional insights for high-variance metrics and statistical proof for low-variance ones. -**2. System-Level Tracking Reveals Hidden Trade-offs (Both Good and Bad)** +- Using a free Mistral model for security reduced that agent's cost to \$0, yet increased total system cost by 14.5 because downstream agents had to work harder with reduced context. However, the experiment also revealed an unexpected 36% latency improvement. Mistral is not just free but significantly faster. LaunchDarkly's user-level tracking captured both effects, enabling an informed decision: enhanced privacy + faster responses for ~\$0.004 more per request is a worthwhile tradeoff. -Using a free Mistral model for security reduced that agent's cost to $0, yet **increased total system cost by 14.58%** because downstream agents had to work harder with reduced context. However, the experiment also revealed an **unexpected 36% latency improvement** - Mistral is not just free but significantly faster. LaunchDarkly's user-level tracking captured both effects, enabling an informed decision: enhanced privacy + faster responses for ~$0.0035 more per request is a worthwhile tradeoff. +- At 87% confidence for latency (as compared to 90% target), the 36% improvement is clear enough for decision-making. Perfect statistical significance is ideal, but 85-89% confidence combined with other positive signals (stable feedback, acceptable cost) can justify deployment when the effect size is large. -**3. "Near Significance" Still Provides Actionable Insights** +## Experiment limitations -At 87% confidence for latency (vs 90% target), the 36% improvement is clear enough for decision-making. Perfect statistical significance is ideal, but 85-89% confidence combined with other positive signals (stable feedback, acceptable cost) can justify deployment when the effect size is large. +We encountered some limitations while running these experiments: -**4. Statistical Rigor Prevents Premature Optimization** +- **Model-as-judge evaluation:** We use Claude to evaluate response quality rather than real users, which represents a limitation of this experimental setup. However, research shows that model-as-judge approaches correlate well with human preferences, as documented in [Anthropic's Constitutional AI paper](https://arxiv.org/abs/2212.08073). -Without LaunchDarkly's Bayesian analysis, you might see "Strict Security is 14.6% more expensive" and optimize prematurely. The 21% confidence reveals high variance - that cost difference could easily be noise. Requiring 90% confidence prevents expensive reactions to random fluctuations. +- **Independent experiments:** While random assignment naturally balances security versions across model versions, preventing systematic bias, you cannot analyze interaction effects between security and model choices. If interaction effects are important to your use case, consider running a proper [factorial experiment design](https://en.wikipedia.org/wiki/Factorial_experiment). +- **Statistical confidence:** LaunchDarkly uses [Bayesian statistics](https://launchdarkly.com/docs/home/experimentation/bayesian) to calculate confidence, where 90% confidence means there's a 90% probability the true effect is positive. This is not the same as p-value < 0.10 from [frequentist tests](https://en.wikipedia.org/wiki/Frequentist_inference). We set the threshold at 90%, rather than 95%, to balance false positives versus false negatives, though for mission-critical features you should consider raising the confidence threshold to 95%. -## Experimental Limitations & Mitigations +## Common mistakes you have avoided -**Model-as-Judge Evaluation** +Here are common mistakes you avoided, and what you did instead: -We use Claude to evaluate response quality rather than real users, which represents a limitation of this experimental setup. However, research shows that model-as-judge approaches correlate well with human preferences, as documented in [Anthropic's Constitutional AI paper](https://arxiv.org/abs/2212.08073). + + + + + + + + + + + + + + + + + + + + + +
Mistake you avoidedWhat you did instead
"Let's run the experiment for a week and see""We defined success criteria upfront" (≥15% improvement threshold)
"We need 90% confidence on every metric to ship""We used 87% confidence and our judgment" (36% latency win was decision-worthy)
"Let's run experiments until all metrics reach significance""We understood variance" (cost/feedback need 5-10x more data than latency)
"Agent-level metrics show the full picture""We tracked user-level workflows" (revealed downstream cost increases)
-**Sample Size** +## What you've accomplished -With approximately 100 users per variation, we're at the minimum threshold for detecting 15-20% effects reliably. For experiments where you expect smaller effects, you should increase the sample to ensure adequate statistical power. +You've built a data-driven optimization engine with statistical rigor through falsifiable hypotheses and clear success criteria. Your predefined success criteria ensure clear decisions and prevent post-hoc rationalization. Every feature investment now has quantified business impact for ROI justification, and you have a framework for continuous optimization through ongoing measurable experimentation. -**Independent Experiments** +Findings from our experiments include: -While random assignment naturally balances security versions across model versions, preventing systematic bias, you cannot analyze interaction effects between security and model choices. If interaction effects are important to your use case, consider running a proper [factorial experiment design](https://en.wikipedia.org/wiki/Factorial_experiment). +- **Unexpected discovery**: Free Mistral model is not only \$0 but also significantly faster than Claude Haiku +- **Cost paradox revealed**: "Free" security agent increased total system costs by forcing downstream agents to compensate +- **Premium model failure**: Claude Opus 4 performed 64% worse than GPT-4o despite costing 33% more +- **Sample size reality**: High-variance metrics (cost, feedback) require 5-10x more data than low-variance ones (latency) -**Statistical Confidence** -LaunchDarkly uses **[Bayesian statistics](https://launchdarkly.com/docs/home/experimentation/bayesian)** to calculate confidence, where 90% confidence means there's a 90% probability the true effect is positive. This is NOT the same as p-value < 0.10 from [frequentist tests](https://en.wikipedia.org/wiki/Frequentist_inference). We set the threshold at 90% (rather than 95%) to balance false positives versus false negatives, though for mission-critical features you should consider raising the confidence threshold to 95%. - -## Common Mistakes We Avoided - -❌ **"Let's run the experiment for a week and see"** - -✅ **We defined success criteria upfront** (≥15% improvement threshold) - -❌ **"We need 90% confidence on every metric to ship"** - -✅ **We used 87% confidence + directional signals** (36% latency win was decision-worthy) +## Troubleshooting -❌ **"Let's run experiments until all metrics reach significance"** +This section includes troubleshooting solutions. -✅ **We understood variance** (cost/feedback need 5-10x more data than latency) +### **Long response times (>20 minutes)** -❌ **"This cost difference looks bad, let's optimize"** +If you see requests taking exceptionally long, the root cause is likely the `semantic_scholar` MCP tool hitting API rate limits, which causes 30-second retry delays. Queries using this tool may take 5-20 minutes to complete. The 2000-second timeout handles this gracefully, but if you need faster responses (60-120 seconds typical), consider removing `semantic_scholar` from tool configurations. You can verify this issue by checking logs for `HTTP/1.1 429` errors indicating rate limiting. -✅ **We checked confidence first** (21% confidence = probably just noise) +### **Cost metrics not appearing** -❌ **"Agent-level metrics show the full picture"** +If `ai_cost_per_request` events aren't showing in LaunchDarkly, first verify that `utils/cost_calculator.py` has pricing configured for your models. Cost is only tracked when requests complete successfully (not on timeout or error). The system flushes cost events to LaunchDarkly immediately after each request completion. To debug, look for `COST CALCULATED:` and `COST TRACKING (async):` messages in your API logs. -✅ **We tracked user-level workflows** (revealed downstream cost increases) +## Beyond this tutorial: Advanced AI Experimentation patterns -## What You've Accomplished +In this guide you ran two experiments: -You've built a **data-driven optimization engine** with statistical rigor through falsifiable hypotheses and clear success criteria. Your predefined success criteria ensure clear decisions and prevent post-hoc rationalization. Every feature investment now has quantified business impact for ROI justification, and you have a framework for continuous optimization through ongoing measurable experimentation. +- **Security‑agent test**: a bundle change, where both prompt/instructions and the model changed. +- **Premium‑model test**: a model‑only change. -## Troubleshooting +AI Configs come in two modes: **prompt‑based** (single‑step completions) and **agent‑based** (multi‑step workflows with tools). Below are additional patterns to explore. -### **Long Response Times (>20 minutes)** +### AI Config experiments -If you see requests taking exceptionally long, the root cause is likely the `semantic_scholar` MCP tool hitting API rate limits, which causes 30-second retry delays. Queries using this tool may take 5-20 minutes to complete. The 2000-second timeout handles this gracefully, but if you need faster responses (60-120 seconds typical), consider removing `semantic_scholar` from tool configurations. You can verify this issue by checking logs for `HTTP/1.1 429` errors indicating rate limiting. +Experiments you can run entirely in AI Configs with no app redeploy include: -### **Cost Metrics Not Appearing** +- **Prompt and template experiments (prompt‑based or agent instructions):** Duplicate a variation and iterate on system/assistant messages or agent instructions to measure adherence to schema, tone, or qualitative satisfaction. Use LaunchDarkly Experimentation to tie those variations to product metrics. -If `ai_cost_per_request` events aren't showing in LaunchDarkly, first verify that `utils/cost_calculator.py` has pricing configured for your models. Cost is only tracked when requests complete successfully (not on timeout or error). The system flushes cost events to LaunchDarkly immediately after each request completion. To debug, look for `💰 COST CALCULATED:` and `COST TRACKING (async):` messages in your API logs. +- **Model‑parameter experiments:** In a single model, vary parameters like `temperature` or `max_tokens`, and optionally add custom parameters you define, for example, an internal `max_tool_calls` or decoding setting, directly on the variation. -## Beyond This Tutorial: Advanced AI Experimentation Patterns +- **Tool‑bundle experiments (agent mode or tool‑enabled completions):** Attach/detach reusable tools from the **Tools Library** to compare stacks, such as `search_v2`, a reranker, or MCP‑exposed research tools, across segments. Keep one variable at a time when possible -### **Other AI experimentation types you can run in LaunchDarkly** +- **Cost/latency trade‑offs:** Compare "slim" versus "premium" stacks by segment. Track tokens, time‑to‑first‑token, duration, and satisfaction to decide where higher spend is warranted. -*Context from earlier:* you ran two experiments: -- **Security‑agent test** → a **bundle change** (both prompt/instructions **and** model changed). -- **Premium‑model test** → a **model‑only** change. +Recommendations include: -AI Configs come in two modes—**prompt‑based** (single‑step completions) and **agent‑based** (multi‑step workflows with tools). Below are additional patterns to explore. +- Use Experimentation for behavior impact (clicks, task success). Use the **Monitoring** tab for LLM‑level metrics (tokens, latency, errors, satisfaction). +- You can't run a guarded rollout and an experiment on the same flag at the same time. Pick one: guarded rollout for risk‑managed releases, experiment for causal measurement. ---- +### Feature flag experiments -#### Experiments you can run **entirely in AI Configs** (no app redeploy) -- **Prompt & template experiments (prompt‑based or agent instructions)** - Duplicate a variation and iterate on system/assistant messages or agent instructions to measure adherence to schema, tone, or qualitative satisfaction. Use LD Experimentation to tie those variations to product metrics. +Experiments you can run with feature flags include: -- **Model‑parameter experiments** - In a single model, vary parameters like `temperature` or `max_tokens`, and (optionally) add **custom parameters** you define (for example, an internal `max_tool_calls` or decoding setting) directly on the variation. +- **Fine‑grained RAG tuning:** k‑values, similarity thresholds, chunking, reranker swaps, and cache policy are typically coded inside your retrieval layer. Expose these as flags or AI Config custom parameters if you want to A/B them. -- **Tool‑bundle experiments (agent mode or tool‑enabled completions)** - Attach/detach reusable tools from the **Tools Library** to compare stacks (e.g., `search_v2`, a reranker, or MCP‑exposed research tools) across segments. Keep one variable at a time when possible +- **Tool‑routing guardrails:** Fallback flows (for example, retry with a different tool/model on error), escalation rules, or heuristics need logic in your agent/orchestrator. Gate those behaviors behind flags and measure with custom metrics. -- **Cost/latency trade‑offs** - Compare “slim” vs “premium” stacks by segment. Track tokens, time‑to‑first‑token, duration, and satisfaction to decide where higher spend is warranted. +- **Safety guardrail calibration:** Moderation thresholds, red‑team prompts, and PII sensitivity levers belong in a dedicated safety service, for example, enterprise as compared to free. -> **Practical notes** -> - Use **Experimentation** for behavior impact (clicks, task success); use the **Monitoring** tab for LLM‑level metrics (tokens, latency, errors, satisfaction). -> - You **can’t** run a guarded rollout and an experiment on the same flag at the same time. Pick one: guarded rollout for risk‑managed releases, experiment for causal measurement. ---- +- **Session budget enforcement:** Monitoring will show token costs and usage, but enforcing per‑session or per‑org budgets (denylist, degrade model, or stop‑tooling) requires application logic. Wrap policies in flags before you experiment. -#### Patterns that **usually need feature flags and/or custom instrumentation** -- **Fine‑grained RAG tuning** - k‑values, similarity thresholds, chunking, reranker swaps, and cache policy are typically coded inside your retrieval layer. Expose these as flags or AI Config custom parameters if you want to A/B them. +### Targeting and segmentation ideas -- **Tool‑routing guardrails** - Fallback flows (e.g., retry with a different tool/model on error), escalation rules, or heuristics need logic in your agent/orchestrator. Gate those behaviors behind flags and measure with custom metrics. +These ideas work with all of the experiment suggestions we listed above: -- **Safety guardrail calibration** - Moderation thresholds, red‑team prompts, and PII sensitivity levers belong in a dedicated safety service or the agent wrapper. Wire them to flags so you can raise/lower sensitivity by segment (e.g., enterprise vs free). +- Target by plan/tier, geo, device, or org using AI Config targeting rules and percentage rollouts. +- Keep variations narrow, one change per experiment, to avoid confounding. Reserve "bundle" tests for tool‑stack comparisons. -- **Session budget enforcement** - Monitoring will show token costs and usage, but enforcing per‑session or per‑org budgets (denylist, degrade model, or stop‑tooling) requires application logic. Wrap policies in flags before you experiment. +Require statistical evidence before shipping configuration changes. Pair each variation with clear success metrics, then A/B test prompt or tool adjustments and use confidence intervals to confirm improvements. When you introduce the new code paths above, protect them behind feature flags so you can run sequential tests, [multi-armed bandits](https://launchdarkly.com/docs/home/multi-armed-bandits) for faster convergence, or change your [experiment design](https://launchdarkly.com/docs/guides/experimentation/designing-experiments) to understand how prompts, tools, and safety levers interact. ---- +## Conclusion: From chaos to clarity -#### Targeting & segmentation ideas (works across all the above) -- Route by **plan/tier**, **geo**, **device**, or **org** using AI Config targeting rules and percentage rollouts. -- Keep variations narrow (one change per experiment) to avoid confounding; reserve “bundle” tests for tool‑stack bake‑offs. +Across this three-part series, you've transformed from hardcoded AI configurations to a scientifically rigorous, data-driven optimization engine. [Part 1](https://launchdarkly.com/docs/tutorials/agents-langgraph) established your foundation with a dynamic multi-agent [LangGraph](https://langchain-ai.github.io/langgraph/) system controlled by [LaunchDarkly AI Configs](https://launchdarkly.com/docs/guides/ai-configs), eliminating the need for code deployments when adjusting AI behavior. [Part 2](https://launchdarkly.com/docs/tutorials/multi-agent-mcp-targeting) added sophisticated targeting with geographic privacy rules, user segmentation by plan tiers, and [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) tool integration for real academic research capabilities. This tutorial, Part 3, completed your journey with statistical experimentation that proves ROI and guides optimization decisions with mathematical confidence rather than intuition. -**Advanced Practices:** Require statistical evidence before shipping configuration changes. Pair each variation with clear success metrics, then A/B test prompt or tool adjustments and use confidence intervals to confirm improvements. When you introduce the new code paths above, protect them behind feature flags so you can run sequential tests, [multi-armed bandits](https://launchdarkly.com/docs/home/multi-armed-bandits) for faster convergence, or change your [experiment design](https://docs.launchdarkly.com/guides/experimentation/designing-experiments) to understand how prompts, tools, and safety levers interact. +You now possess a defensible AI system that adapts to changing requirements, scales across user segments, and continuously improves through measured experimentation. Your stakeholders receive concrete evidence for AI investments, your engineering team deploys features with statistical backing, and your users benefit from optimized experiences driven by real data rather than assumptions. The chaos of ad-hoc AI development has given way to clarity through systematic, scientific product development. -## From Chaos to Clarity +{version === "LaunchDarkly docs" && ( -Across this three-part series, you've transformed from hardcoded AI configurations to a scientifically rigorous, data-driven optimization engine. **[Part 1](tutorial_1.md)** established your foundation with a dynamic multi-agent [LangGraph](https://langchain-ai.github.io/langgraph/) system controlled by [LaunchDarkly AI Configs](https://launchdarkly.com/ai-config/), eliminating the need for code deployments when adjusting AI behavior. **[Part 2](tutorial_2.md)** added sophisticated targeting with geographic privacy rules, user segmentation by plan tiers, and [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) tool integration for real academic research capabilities. **[Part 3](tutorial_3.md)** completed your journey with statistical experimentation that proves ROI and guides optimization decisions with mathematical confidence rather than intuition. +Your 14-day trial begins as soon as you sign up. Get started in minutes using the in-app Quickstart. You'll discover how easy it is to release, monitor, and optimize your software.

-You now possess a defensible AI system that adapts to changing requirements, scales across user segments, and continuously improves through measured experimentation. Your stakeholders receive concrete evidence for AI investments, your engineering team deploys features with statistical backing, and your users benefit from optimized experiences driven by real data rather than assumptions. The chaos of ad-hoc AI development has given way to clarity through systematic, scientific product development. +Want to try it out? Start a trial. -## Resources +)} -- **[LaunchDarkly Experimentation Docs](https://launchdarkly.com/docs/home/experimentation)** - Deep dive into statistical methods ---- +## Related tutorials -**Remember:** Every AI decision backed by data is a risk avoided and a lesson learned. Start small, measure everything, ship with confidence. +- [Build a LangGraph Multi-Agent system in 20 Minutes](https://launchdarkly.com/docs/tutorials/agents-langgraph) - Part 1 of this series: build the agent system that the experiments here measure +- [Smart AI Agent Targeting with MCP Tools](https://launchdarkly.com/docs/tutorials/multi-agent-mcp-targeting) - Part 2: add user, geo, and tier targeting before experimenting on it +- [Beyond n8n for Workflow Automation: Agent Graphs](https://launchdarkly.com/docs/tutorials/agent-graphs) - Externalize the multi-agent topology with visual graph builder and per-node metrics +- [When to add online evals](https://launchdarkly.com/docs/tutorials/when-to-add-online-evals) - Surface the metrics experiments measure with judge-based scoring on live traffic +- [Offline Evaluation of RAG-Grounded Answers](https://launchdarkly.com/docs/tutorials/offline-evals) - Lock in an offline baseline before running A/B tests +- [Evaluate LLM code generation with LLM-as-judge evaluators](https://launchdarkly.com/docs/tutorials/custom-evals-claude-code) - Build custom judges to produce the quality metrics your experiments compare \ No newline at end of file From c406f94809b5efa7765689d46ccc74b7b99bd98a Mon Sep 17 00:00:00 2001 From: Scarlett Attensil Date: Sun, 17 May 2026 15:37:14 -0700 Subject: [PATCH 4/7] Restore AWS Bedrock setup path to README The published agents-langgraph.mdx is API-key-only and never documented the Bedrock SSO path the repo code supports. Re-add the Option A/B provider setup and Bedrock inference-profile guidance to the repo README (docs mdx intentionally left unchanged), using current model IDs. --- README.md | 106 ++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 103 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 85ca49d..1036470 100644 --- a/README.md +++ b/README.md @@ -69,8 +69,17 @@ You'll need: - **Python 3.9+** with `uv` package manager ([install uv](https://docs.astral.sh/uv/getting-started/installation/)) - **LaunchDarkly account** ([sign up for free](https://app.launchdarkly.com/signup)) + +**Choose your AI provider setup:** + +**Option A: AWS Bedrock (Recommended)** +- **AWS Account** with Bedrock access and SSO configured +- **No API keys required** - uses AWS SSO authentication +- **Cost-effective** - enterprise-grade with cross-region failover + +**Option B: Direct API Keys (Traditional)** - **OpenAI API key** (required for RAG architecture embeddings) -- **Anthropic API key** (required for Claude models) or **OpenAI API key** (for GPT models) +- **Anthropic API key** (for Claude models) or **OpenAI API key** (for GPT models) ## Step 1: Clone and Configure (2 minutes) @@ -139,15 +148,106 @@ First, you need to get your LaunchDarkly SDK key by creating a project: -Now edit `.env` with your keys: +Now configure your authentication method in `.env`: + +### Option A: AWS Bedrock Setup (Recommended) + ```bash +# LaunchDarkly Configuration LD_SDK_KEY=your-launchdarkly-sdk-key # From step above + +# AWS Bedrock Configuration +AUTH_METHOD=sso # Use AWS SSO authentication +AWS_REGION=us-east-1 # Your AWS region +AWS_PROFILE=your-sso-profile-name # Your AWS SSO profile name + +# Optional: Bedrock Embedding Configuration +BEDROCK_EMBEDDING_DIMENSIONS=1024 # Options: 256, 512, 1024 +BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0 +``` + +**Then configure AWS SSO:** +```bash +# Configure AWS SSO (one-time setup) +aws configure sso --profile your-sso-profile-name + +# Login to AWS SSO (run when token expires) +aws sso login --profile your-sso-profile-name + +# Test your access +aws bedrock list-foundation-models --region us-east-1 --profile your-sso-profile-name +``` + +### Option B: Direct API Keys Setup + +```bash +# LaunchDarkly Configuration +LD_SDK_KEY=your-launchdarkly-sdk-key # From step above + +# Direct API Configuration +AUTH_METHOD=api-key # Use direct API keys (default) OPENAI_API_KEY=your-openai-key # Required for RAG embeddings ANTHROPIC_API_KEY=your-anthropic-key # Required for Claude models ``` This sets up a **LangGraph** application that uses LaunchDarkly to control AI behavior. Think of it like swapping actors, directors, even props mid-performance without stopping the show. -Do not check the `.env` into your source control. Keep those secrets safe! + +**Security Note:** Do not check the `.env` into your source control. Keep those secrets safe! + +### 🚨 **Common AWS SSO Issue:** + +If you get `AccessDeniedException` errors, verify your Python code is using the correct AWS profile: + +```bash +# Check which AWS account your profile uses +aws sts get-caller-identity --profile your-sso-profile-name + +# If the account numbers don't match your error message, add AWS_PROFILE to your .env +``` + +**The error message will show the account number your Python code is using.** Make sure it matches your SSO profile account. + +### 🚨 **Bedrock Model ID Requirements:** + +When configuring AI models in LaunchDarkly for Bedrock, you should use **inference profile IDs** (with region prefix): + +**✅ BEST PRACTICE - Inference Profile IDs (with region prefix):** +``` +us.anthropic.claude-sonnet-4-6-v2:0 +us.anthropic.claude-3-7-sonnet-20250219-v1:0 +eu.anthropic.claude-haiku-4-5-20251001-v2:0 +``` + +**⚠️ AUTO-CORRECTED - Direct Model IDs (will be fixed automatically):** +``` +anthropic.claude-3-7-sonnet-20250219-v1:0 → us.anthropic.claude-3-7-sonnet-20250219-v1:0 +anthropic.claude-sonnet-4-6-v2:0 → us.anthropic.claude-sonnet-4-6-v2:0 +``` + +**How Auto-Correction Works:** + +The system automatically converts direct model IDs to inference profile IDs to prevent `ValidationException` errors from Bedrock. The region prefix is determined by: + +1. **`BEDROCK_INFERENCE_REGION`** env var (if set) - explicit user preference +2. **`AWS_REGION`** env var (e.g., `us-east-1` → `us` prefix) - automatic detection +3. **Default to `us`** if neither is set + +**Configuring Region Prefix:** + +To use European inference profiles, add to your `.env`: +```bash +BEDROCK_INFERENCE_REGION=eu # Force EU inference profiles +``` + +**Finding Available Inference Profiles:** +```bash +# List available Bedrock inference profiles +aws bedrock list-inference-profiles --region us-east-1 --profile your-sso-profile-name +``` + +**Why Inference Profiles?** + +Bedrock requires inference profile IDs for on-demand throughput. The region prefix (`us.`, `eu.`, `ap.`, etc.) enables cross-region inference profiles for better availability and failover. ## Step 2: Add Your Business Knowledge (2 minutes) From 93f24618f51a721d2e5720f00ab519c077d2257b Mon Sep 17 00:00:00 2001 From: Scarlett Attensil Date: Sun, 17 May 2026 19:52:15 -0700 Subject: [PATCH 5/7] Point bootstrap manifest at agents-tut-1-langgraph project DIVERGENCE FROM TUTORIAL: The published Part 2 tutorial documents the project key as 'multi-agent-chatbot' (and the README naming-requirements box still says to use that name). This repo's working LaunchDarkly project is 'agents-tut-1-langgraph', so the bootstrap manifest is pointed there to make create_configs.py run against the real project. Anyone following the tutorial verbatim should set this back to 'multi-agent-chatbot' (or their own project key). This is an environment-specific override, not a tutorial content change. --- bootstrap/ai_config_manifest.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bootstrap/ai_config_manifest.yaml b/bootstrap/ai_config_manifest.yaml index 54dcecc..fcc1022 100644 --- a/bootstrap/ai_config_manifest.yaml +++ b/bootstrap/ai_config_manifest.yaml @@ -10,7 +10,7 @@ # DO NOT modify instructions here after bootstrap - use LaunchDarkly instead! project: - key: "multi-agent-chatbot" # Your LaunchDarkly project key + key: "agents-tut-1-langgraph" # Your LaunchDarkly project key environment: - key: production From 3d9a949f251d87ad777ce115e2fcaaf24b07c11b Mon Sep 17 00:00:00 2001 From: Scarlett Attensil Date: Sun, 17 May 2026 19:59:27 -0700 Subject: [PATCH 6/7] Revert "Point bootstrap manifest at agents-tut-1-langgraph project" This reverts commit 93f24618f51a721d2e5720f00ab519c077d2257b. --- bootstrap/ai_config_manifest.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bootstrap/ai_config_manifest.yaml b/bootstrap/ai_config_manifest.yaml index fcc1022..54dcecc 100644 --- a/bootstrap/ai_config_manifest.yaml +++ b/bootstrap/ai_config_manifest.yaml @@ -10,7 +10,7 @@ # DO NOT modify instructions here after bootstrap - use LaunchDarkly instead! project: - key: "agents-tut-1-langgraph" # Your LaunchDarkly project key + key: "multi-agent-chatbot" # Your LaunchDarkly project key environment: - key: production From 039ff453aa3ccd9bdc75573a0c60b66f416ed67d Mon Sep 17 00:00:00 2001 From: Scarlett Attensil Date: Mon, 18 May 2026 15:07:36 -0700 Subject: [PATCH 7/7] Remove unused AIAgentConfigRequest import Dead import left over from the SDK 0.20 roll-forward; the new agent_config() API path does not use it. --- config_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/config_manager.py b/config_manager.py index 1d85368..a1976f0 100644 --- a/config_manager.py +++ b/config_manager.py @@ -8,7 +8,7 @@ from pathlib import Path import ldclient from ldclient import Context -from ldai import LDAIClient, AIAgentConfigRequest, AIAgentConfigDefault, ModelConfig, ProviderConfig +from ldai import LDAIClient, AIAgentConfigDefault, ModelConfig, ProviderConfig from ldai.tracker import FeedbackKind from dotenv import load_dotenv from utils.logger import log_student, log_debug