Agents
Multi-Agent Systems
Architectures where multiple AI agents collaborate on a task, each handling a specialized role.
In depth
Architectures where multiple AI agents collaborate on a task, each handling a specialized role. One agent might research while another writes code and a third reviews it. Multi-agent patterns include orchestrator-worker, pipeline, and swarm topologies.
Example
In practice, developers reach for Multi-Agent Systems when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Multi-Agent Systems?
Architectures where multiple AI agents collaborate on a task, each handling a specialized role.
Why does Multi-Agent Systems matter for AI developers?
Multi-Agent Systems sits in the Agents part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Multi-Agent Systems?
Developers Digest publishes tutorials and videos that cover Agents topics including Multi-Agent Systems. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Agents
Agent Handoffs
A multi-agent mechanism where one agent passes control of the conversation to a more specialized agent, rather than calling it as a subroutine.
Agents
Agent Swarm
A multi-agent pattern where many lightweight agents work on sub-tasks simultaneously without a central orchestrator.
Agents
Tool Chaining
A pattern where an AI agent uses the output of one tool call as the input for the next, building a multi-step pipeline of actions.
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