Implementing Multi-Agent Team

We have successfully built a functional, single-agent Research Assistant. This monolithic agent is a powerful tool, capable of executing a complex, multi-step plan from start to finish. However, as agentic systems grow in complexity and capability, the monolithic design can present challenges. A single, highly detailed instruction prompt can become difficult to manage, debug, and extend. When we want to add a new tool or change the workflow, we risk breaking the overall logic of the entire prompt.

To build more professional, scalable, and maintainable systems, we can draw inspiration from a core principle of modern software engineering: the separation of concerns. This leads us to refactor our single agent into a multi-agent system. This lesson will guide us through the process of transforming our monolithic researcher into a collaborative team of specialized agents, each with a single, well-defined responsibility.

The case for a multi-agent system

A multi-agent system is an application composed of multiple, distinct agents that collaborate or coordinate to achieve a larger goal. By breaking down a complex problem into smaller, more manageable sub-tasks and assigning each to a specialized agent, we gain several significant advantages that are crucial for building professional-grade applications, such as:

• Modularity: Each agent becomes a self-contained module with a single responsibility. An agent that only knows how to search Wikipedia is far simpler to develop, test, and debug in isolation than a single agent that must know how to do everything. If our Wikipedia search is failing, we know exactly which component to inspect.

• Specialization: By isolating tasks, we can fine-tune each agent for its specific purpose. We can write a highly specialized instruction prompt for each one. Furthermore, we can use different LLMs for different agents. A complex reasoning task may require a powerful model like Gemini Advanced, while a simple formatting task may only need a faster, cheaper model. This allows us to optimize both performance and cost.

• Maintainability and scalability: In a multi-agent system, adding new functionality is significantly easier. If we wanted to add a new research capability, for example, a tool to search a financial news API, we would not need to rewrite the core logic of our entire system. Instead, we could just build a new, self-contained FinancialNewsAgent and add ...