Building AI Agents in LlamaIndex: Multi-Agent System

In our previous lesson, we built a capable single-agent system—an intelligent assistant that could track orders, process returns, and help users find products. It handled customer queries quite well using tools and LLM-powered reasoning.

But let’s imagine a new situation.

A user says:
“I want to return my last order, and also suggest me a smartwatch that pairs well with a Samsung phone under $200.”

It sounds straightforward, but behind the scenes, this query involves two very different types of tasks:

• Handling a return (which involves order data and processes)

• Recommending a new product (which involves filtering, preferences, maybe even summarizing reviews)

Sure, our single FunctionAgent could try to do both.

But that’s like asking one employee to handle customer support, be your personal shopping assistant, and summarize reviews all at once.

Instead of overloading a single agent, what if we gave each of these responsibilities to a specialized agent?

Just like a real-world team, we’re now thinking in terms of collaboration. And just like that, we step into the world of multi-agent systems.