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.

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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

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Agent Roles in This Example

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

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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.

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Routing Between Agents

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

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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

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