Beyond Chat: Introducing Bedrock Agents

Until now, we have used AI as a "Reference" (Chat/RAG). Bedrock Agents move AI into the realm of Action. An Agent is a model that can carry out complex, multi-step tasks by calling External Tools (like checking your email, searching a database, or booking a flight).

1. The Core Difference

• Chatbot: Answers questions.
• RAG System: Answers questions based on files.
• Agent: Executes a workflow to achieve a goal.

2. Agent Architecture

A Bedrock Agent consists of three main parts:

1. Instruction: The goal and persona of the agent.
2. Action Groups: The "Hands" of the agent—APIs or Lambda functions it can call.
3. Knowledge Bases: (Optional) The "Memory" of the agent.

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3. Visualizing the Agent Loop

graph TD
    User[Task: 'Book me a trip to NYC'] --> Agent[Agent Brain]
    Agent -->|Reasoning| Plan[Plan: Search Flights, Check Hotel, Confirm]
    Plan -->|Call| Tool1[Flight API]
    Tool1 -->|Data| Agent
    Agent -->|Next Step| Tool2[Hotel API]
    Tool2 -->|Data| Agent
    Agent --> Answer[Response: 'Trip Booked for $500. Check your email.']

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4. Why Bedrock Agents?

• Managed Orchestration: You don't have to write the "If/Then" logic. The LLM decides which tool to call next based on the user's progress.
• Security: Agents are fully integrated with AWS IAM and Secrets Manager.
• Traceability: You can see every step of the agent's "Thought Process" in the console.

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Summary

• Agents are autonomous executors of tasks.
• They use Reasoning to choose between different Tools.
• They rely on Action Groups to interact with the real world.
• Bedrock handles the complex "Logic Loop" so you don't have to.