Introduction to Multi-Agent Systems

1. Introduction to Multi-Agent Systems

Welcome to the final chapter of this course! In this lesson, we're going from multi-step agents to multi-agent systems that can handle more complex, multi-part tasks.

2. Scenario: Career Advisor Agent

Imagine this scenario: You just built a career advisor agent for helping professionals transition into data science. A marketing specialist then asks the agent the following: "I want to switch from marketing to data science. Please help me update my resume, find companies hiring, prepare for interviews, and understand salaries." This isn't just one task. It's a bundle of them. Updating resumes, job searching, interview prep, and salary research all require different tools and workflows.

3. Why Use Multi-Agent Systems?

And that's where multi-agent systems come in. Instead of relying on a single, all-knowing agent, we can orchestrate a team of specialized agents to collaborate and get the job done. Each specialist agent maintains its own focused context and expertise, preventing information overload and task confusion. It's similar to how a team leader hires multiple professionals with different skills to work on a large-scale project.

4. A Specialized Resume Agent

Here's what a specialized agent for resume building might look like: It has dedicated tools for skill translation between industries and generating optimal resume layouts. The name and description parameters are crucial: they help the manager agent decide which specialist should handle each part of the request.

5. A Company Research Specialist

Let's add another specialist to the team: an agent focused on researching companies. Just like the resume agent, this one brings its own tools and expertise. We can create additional specialized agents for interview preparation and salary negotiation tactics using the same pattern.

6. The Manager Agent

Once all the specialists are defined, we bring them together under a single coordinating agent: the manager. This manager agent doesn't do the work itself; it delegates tasks to the appropriate specialists based on their descriptions. It coordinates our four specialist agents using the managed_agents parameter. And just like that, you've built your first multi-agent system! Manager agents tend to benefit from models with strong reasoning and coordination capabilities, since they need to plan, break down tasks, and assign work effectively.

7. Multi-Agent Orchestration

When a request comes in, the manager agent analyzes the task, uses each sub-agent's name and description, and delegates the appropriate parts to the right specialists. This is the power of a multi-agent system: clear role separation, intelligent delegation, and collaborative execution.

8. Let's practice!

Now, you're ready to build your own multi-agent system in the exercises!

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