The Power of Collaboration

In Module 8, we built a single agent. But what happens when the task is too large for one "Brain"? Imagine an agent that needs to write a software feature: it must design the architecture, write the code, run the unit tests, and fix any bugs. For a single model, this "Logic Chain" is long and prone to error.

The solution is a Multi-Agent System. Instead of one generalist agent, we build a team of specialists that collaborate under the direction of an Orchestrator.

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1. Multi-Agent Design Patterns

As a Professional Developer, you must choose the right "Social Structure" for your agents.

Pattern A: The Supervisor (Hub and Spoke)

A single "Manager Agent" receives the goal, chooses a "Worker Agent" to handle a sub-task, reviews the work, and then assigns the next piece.

• Best For: Tasks with a clear sequence (e.g., Content Creation -> Review -> Publishing).

Pattern B: The Peer-to-Peer (Collaborative)

Agents talk to each other directly without a manager.

• Best For: Creative brainstorming or complex problem solving where agents need to "argue" to find the best result.

Pattern C: Hierarchical

A supervisor manages other supervisors, who in turn manage workers.

• Best For: Entire business processes (e.g., an "Automated Software Department").

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2. Architecting the Workflow

graph TD
    User[User Goal] --> S[Supervisor Agent]
    S -->|Drafting| A1[Agent: Researcher]
    S -->|Coding| A2[Agent: Developer]
    S -->|Quality| A3[Agent: Reviewer]
    
    A1 --> S
    A2 --> S
    A3 --> S
    S --> Final[Final Delivery]
    
    style S fill:#ff9900,color:#fff

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3. Implementing Multi-Agents on AWS

How do you build this?

1. AWS Step Functions: Use the "Map" or "Choice" states to route between different Bedrock Agents.
2. Bedrock Agents Multi-Agent Orchestration: A new feature that allows one Bedrock Agent to call another Bedrock Agent as a tool.
3. LangGraph: An open-source framework (often hosted on AWS Lambda/Fargate) designed specifically for cyclic, multi-agent graphs.

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4. Why Multi-Agent is Better

• Modular Testing: You can test the "Researcher" agent separately from the "Developer" agent.
• Reduced Hallucination: When a "Reviewer" agent looks at a "Developer" agent's work, it provides a "Reality Check" that reduces errors by up to 40%.
• Token Efficiency: Specialist agents can have smaller, more focused system prompts, saving costs compared to one giant "Do Everything" prompt.

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5. Challenges: The "Telephone Game"

The risk of multi-agent systems is that information is lost as it passes from agent to agent. The Pro Solution: Use a Centrally Managed State (like an S3 file or a DynamoDB record) that all agents can read from. Instead of passing the whole document, they pass a "Reference ID" to the shared state.

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6. Real-World Scenario: The Automated Helpdesk

• Agent 1 (Triage): Detects sentiment and language.
• Agent 2 (RAG Searcher): Finds the relevant solution in the Knowledge Base.
• Agent 3 (Policy Validator): Checks if the solution follows company safety guidelines.
• Agent 4 (Writer): Drafts the final email to the customer.

Each agent does ONE thing perfectly.

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Knowledge Check: Test Your Multi-Agent Knowledge

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Summary

Don't build a single "God-Agent." Build a specialized team. By using multi-agent patterns, you increase the scale and reliability of your AI systems. In the next lesson, we will look at Handling Long-Running Agent Tasks.

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Next Lesson: Persistence in Action: Handling Long-Running Agent Tasks