Inside the Brain: The Reasoning Loop

When you give a Bedrock Agent a complex task, it doesn't just "Guess." It follows a pattern called ReAct (Reason + Act). This is a structured way of thinking and doing that ensures the agent reaches the goal logically.

1. The 4 Steps of ReAct

1. Thought: "The user wants the weather in NYC. I have a weather tool. I should call it."
2. Action: Calls get_weather(city='NYC').
3. Observation: Receives "72 degrees and sunny."
4. Final Response: "It's a beautiful 72 degree day in NYC."

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2. Multi-step Reasoning

If the task is harder (e.g., "Find my order and tell me if it will arrive before my vacation on Friday"), the loop runs multiple times:

• Loop 1: Call get_order_status. (Observation: Arrives Thursday).
• Loop 2: Call check_calendar. (Observation: Vacation starts Friday).
• Loop 3: Synthesize the two facts. (Response: "Yes, it arrives one day before your trip.")

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3. Visualizing the Loop

graph TD
    User[Complex Question] --> T{Thought}
    T --> A[Action: Call Tool]
    A --> O[Observation: Data Result]
    O --> T2{Is task done?}
    T2 -->|No| T
    T2 -->|Yes| Final[Show Result]

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4. Why This Matters

Because the reasoning is "Visible" (Module 10 Traceability), you can debug where the agent failed. Did it have the wrong Thought? Or did the tool give a bad Observation? This transparency is the key to building reliable enterprise AI.

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Summary

• ReAct is the industry standard for agentic reasoning.
• Thought blocks allow the agent to explain its logic.
• Observations are the "Reality" the agent uses to refine its next thought.
• Complex tasks require Multi-turn loops through the brain.