Multi-Agent Systems

Overview

In advanced scenarios, you can use multiple AI Agents to handle different domains of knowledge or task types. Each agent has its own instructions and set of tools - enabling modular, scalable workflows.

This setup improves clarity, separates responsibilities, and allows for more focused instruction design.

When to Use

Use multiple AI agents when:

Your workflow spans distinct areas like weather, finance, or general web lookup
You want to keep agent logic focused (no bloated all-in-one prompts)
You need modular agents that are easier to test, reuse, or extend
Different agents require different tools, models, or language settings

Agent Roles in This Example

Agent	Task
`main_agent`	Parses the user input and routes to the right sub-agent
`finance_agent`	Handles currency conversion and crypto price checks
`weather_agent`	Handles weather summaries and current condition lookups

Each sub-agent is connected only to tools relevant to its domain (e.g., finance_agent → currency_converter, crypto_price_checker).

Tool Isolation

Keep each agent connected to only what it needs.

weather_agent → quick_weather_summary, current_weather_via_coordinates
finance_agent → currency_converter, crypto_price_checker
web_search_tool is accessible globally or from main_agent

This way the main agent acts like a dispatcher, and the sub-agents remain focused.

Routing Between Agents

The main_agent can detect intent from the message itself and trigger the right branch of logic.

Best Practices

Assign a clear responsibility to each agent
Keep agent prompts short and purpose-specific
Use tool descriptions and names that match their role
Let the main agent route and orchestrate, not perform everything
Test sub-agents independently