Multi-Agent Systems

For complex tasks, you can design systems where multiple specialized agents collaborate. Each agent has a clear responsibility, and one agent can call another as a tool.

The main benefits are separation of concerns (each agent focuses on one thing) and easier debugging (you can isolate issues to a specific agent).

Example: Company Research

Flow: 6_multi_agent_research.yaml

This flow demonstrates a two-agent system for competitor research:

Agent	Specialization	Tools	Responsibility
Research Agent	Web research and data gathering	Tavily web search	Find factual, current information
Main Analyst Agent	Analysis and synthesis	Research agent (used as a tool)	Create structured reports

How it works:

Input: company name (e.g., "kestra.io")
Main agent receives prompt: "Research this company"
Main agent calls the research agent tool: "Find information about kestra.io"
Research agent uses Tavily to gather data from the web
Research agent returns findings to the main agent
Main agent structures the findings into a final JSON output

The key pattern here is using AIAgent as a tool. The main agent treats the research agent exactly like a web search or database call — it invokes it when needed and works with whatever comes back.

Best Practices

Define clear responsibilities: each agent should have a specific role and stay within it
Monitor token usage: multiple agents means multiple LLM calls — costs add up
Document agent purposes: make the system maintainable by describing what each agent does in your flow and task descriptions

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