Observability: Seeing the Hidden Logic

AI can be a "Black Box." When an agent gives a weird answer or calls the wrong tool, you need to know Why. In AWS, we use CloudWatch and Agent Tracing to pull back the curtain.

1. Agent Traces (The Internal Monologue)

Bedrock provides a real-time Trace that shows the agent's thought process.

• Pre-processing: How the agent interpreted the user query.
• Orchestration: The step-by-step ReAct loop (Thoughts + Tool Calls).
• Post-processing: The final check before the answer is sent.

2. CloudWatch Logs

For production debugging, you enable CloudWatch Logs on your Bedrock settings.

• It records Every Prompt, Every Response, and Every Tool Call error.
• Search Tip: Use "CloudWatch Logs Insights" to search for phrases like errorMessage or fail across thousands of agent invocations.

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3. Visualizing the Trace

graph TD
    User[Query] --> Pre[Pre-processing: Is this safe?]
    Pre --> Orch[Orchestration: Think -> Act -> Observe]
    Orch --> Post[Post-processing: Does it meet the policy?]
    Post --> Final[UI]
    
    Pre -.-> CW[CloudWatch Log]
    Orch -.-> CW
    Post -.-> CW

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4. Why Tracing is Crucial

If a user says "Your AI was rude yesterday," CloudWatch allows you to find that exact RequestID and see the context. Without logs, you are just "Guessing" how to fix the AI.

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Summary

• Tracing reveals the internal thinking of the agent in real-time.
• CloudWatch stores permanent records of AI interactions.
• Insights help you find errors at scale in production.
• Post-processing traces show you if a guardrail blocked a response.