Multi-Agent System
A multi-agent system splits a job across several specialised AI agents that coordinate through a shared plan, message bus or orchestrator.
/ quick answer
A multi-agent system (MAS) is an architecture where multiple LLM agents, each with a narrow role and tool set, work on one goal. Coordination usually follows one of three shapes: supervisor (one orchestrator delegates), pipeline (agents run in sequence), or swarm (agents hand off peer-to-peer). MAS buys specialisation and parallelism at the cost of latency, token spend and debuggability.
/ frequently asked
What is Multi-Agent System?
A multi-agent system (MAS) is an architecture where multiple LLM agents, each with a narrow role and tool set, work on one goal. Coordination usually follows one of three shapes: supervisor (one orchestrator delegates), pipeline (agents run in sequence), or swarm (agents hand off peer-to-peer). MAS buys specialisation and parallelism at the cost of latency, token spend and debuggability.
What is an example of Multi-Agent System?
A content system with a Researcher agent, a Writer agent and an Editor agent supervised by an orchestrator that reruns the Writer whenever the Editor rejects the draft.
Why does Multi-Agent System matter?
A multi-agent system splits a job across several specialised AI agents that coordinate through a shared plan, message bus or orchestrator.
What should I learn after Multi-Agent System?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →LangGraph Framework
LangGraph is a Python library built on LangChain that enables building stateful, multi-actor applications with LLMs by modeling agentic workflows as graphs. It allows for defining complex agent behaviors, including loops and conditional logic, crucial for advanced AI agent orchestration.
- →CrewAI Framework
CrewAI is a framework for orchestrating role-playing autonomous AI agents, enabling them to collaborate, delegate, and execute tasks as a team. It focuses on clearly defined roles, tasks, and a shared goal to build highly collaborative AI systems.
- →AutoGen Framework
AutoGen is a framework from Microsoft that enables the development of multi-agent conversation systems, allowing agents to communicate, negotiate, and collaborate to achieve complex tasks through dynamic dialogue.
- →Agent-to-Agent Protocol (A2A)
An Agent-to-Agent (A2A) protocol defines the communication standards and rules that enable autonomous AI agents to interact and exchange information or services directly with each other without human intervention. This foundational concept is crucial for building complex, collaborative multi-agent systems.
Related workflows
Turn this into a repeatable process.
- →Build a Multi-Agent Content Team
Researcher, writer and editor agents under a supervisor, producing publishable drafts.
- →Design an Agent Architecture Before Writing Code
A one-page design process that prevents the most expensive agent rebuilds.
- →Automate Agent Task Delegation
This workflow automates the process of an initiating AI agent delegating a complex task to a specialized recipient AI agent using a standardized Agent-to-Agent (A2A) protocol, ensuring efficient task handoff and execution. It streamlines multi-agent collaboration for defined objectives.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
Related tool stacks
The tools that run it in production.
- →Multi-Agent Orchestration Stack
Tooling for coordinating several specialised agents with reliable handoffs.
- →AutoGen Agent Research Stack
This stack outlines the core components for building an autonomous research agent system using AutoGen, focusing on dynamic information retrieval, analysis, and report generation.
- →A2A Orchestration Stack
This stack comprises the essential tools and frameworks for designing, deploying, and managing Agent-to-Agent (A2A) communication protocols and multi-agent system orchestration. It provides the infrastructure for complex AI agent collaboration.
- →Agent Architecture Stack
The minimum tooling to design, run and observe a production agent.
Related prompts
Reusable prompts for this job.
- →Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
- →Crypto Agent Design Prompt
Specifies an AI crypto agent's tools, permissions, guardrails and evaluation before a single line is written.
- →Wallet Monitoring Agent Prompt
System prompt for a read-only agent that watches addresses, filters noise and reports only decision-relevant activity.
Related use cases
How people apply it, and what came out.
- →Ecommerce Team Runs a Multi-Agent Catalog Pipeline
Three agents enrich, write and QA 12,000 product listings.
- →SaaS Cuts First-Response Time from 6h to 4min with an Agent
A 12-person SaaS shipped a tier-1 support agent and kept humans on exceptions only.
- →Build An AI Crypto Research Agent
A read-only research agent produced daily briefings on a 30-token watchlist, cutting a 90-minute manual routine to a 10-minute review.
Comparisons & alternatives
Pick between the options.
- →Single Agent vs Multi-Agent System
One well-equipped agent beats a crowd for most jobs; multi-agent wins on genuinely separable, parallel work.
- →AI Agent vs Trading Bot
A trading bot executes fixed rules deterministically; an AI agent interprets context and decides which steps to take — powerful for research, risky for execution.