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.
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 for AI and automation?
A multi-agent system splits a job across several specialised AI agents that coordinate through a shared plan, message bus or orchestrator. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
/ 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 Architecture
Agent architecture is the structural blueprint of an AI agent: the model, the planning loop, the tools, the memory layer and the guardrails that decide how it acts.
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.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
- →AI Agent + Web3 Execution
Give an AI agent on-chain capability safely: read freely, propose openly, and sign only inside hard-coded limits.
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.
- →Agent Architecture Stack
The minimum tooling to design, run and observe a production agent.
- →Multi-Agent Orchestration Stack (CrewAI)
This stack outlines the essential components for deploying a robust multi-agent system using CrewAI, focusing on agent definition, tool integration, and monitoring for collaborative task execution.
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.