Building Multi-Agent Systems

As your AI applications grow, you might find that a single Agent, governed by a single System Prompt, starts making mistakes. If you tell an Agent to be a witty marketing copywriter AND a strict JSON data extractor, its "brain" gets pulled in two different directions.

The solution is a Multi-Agent System.

What is a Multi-Agent System?

Instead of building one massive Agent that tries to do everything, you build multiple specialized Agents that talk to each other.

Think of it like a corporate office. You don't have one employee who does sales, engineering, HR, and accounting. You have a Manager who delegates tasks to Specialists.

The Architecture

Let's build an AI software development team.

1. The Manager Agent:

   • Prompt: "You are a Project Manager. Read the user's feature request. Break it down into code tasks. Pass the tasks to the Coder Agent. When the Coder is done, pass the code to the QA Agent. Finally, deliver the result to the user."
   • Tools: assign_task_to_coder(), assign_task_to_qa()

2. The Coder Agent:

   • Prompt: "You are a Senior Developer. Write React code based on the Manager's instructions. Only output raw code."
   • Tools: None. Just outputs text.

3. The QA Agent:

   • Prompt: "You are a ruthless QA Tester. Review the Coder's code. If you find a bug, reject it and send it back to the Manager with an explanation."
   • Tools: reject_code(), approve_code()

How the Code Works

In your Node.js backend, these "Tools" aren't calling external weather APIs. They are literally just calling other Claude API endpoints!

When the Manager outputs assign_task_to_coder("Write a button"): Your Node.js app intercepts that tool, creates a new payload with the Coder's System Prompt, passes the text "Write a button" to it, and waits for Claude (acting as the Coder) to return the code.

Your Node.js app then packages that code into a tool_result block and hands it back to the Manager.

Benefits of Multi-Agent Systems

1. Separation of Concerns: Each Agent has one hyper-focused System Prompt, drastically reducing confusion and hallucinations.
2. Specialized Models: The Manager might use Opus (for deep logic), the Coder might use Sonnet (for fast coding), and the QA might use Haiku (for quick reviews). This optimizes costs.
3. Autonomy: By defining the rules of interaction (e.g., the QA Agent is allowed to reject code and trigger a rewrite), the system can loop and self-correct multiple times before the human user ever sees the final output.

Multi-Agent Systems are the cutting edge of modern AI engineering. They transform LLMs from simple chatbots into autonomous digital workforces.