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Multi-Agent Systems

What are multi-agent systems? Architectures where multiple AI agents collaborate to solve complex tasks.

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Multi-Agent Systems — AI Glossary

Multi-agent systems (MAS) are architectures where multiple autonomous AI agents interact — collaborating, delegating, or competing — to accomplish tasks that would be difficult or impossible for a single agent. Each agent operates with its own context, tools, and objectives, while a coordination layer manages communication and task routing between them. Think of it as the difference between one developer working alone and a team with specialized roles splitting the work.

Why Multi-Agent Systems Matter

Single-agent architectures hit hard limits. One agent juggling code generation, testing, deployment, and monitoring runs into context window constraints, latency bottlenecks, and compounding errors. Multi-agent systems solve this by decomposing work across specialized agents — a planner agent breaks down tasks, a coder agent writes implementations, a reviewer agent checks quality.

This pattern is now central to agentic coding tools. Claude Code's agent teams spawn sub-agents for parallel execution across large codebases. OpenAI's Swarm framework and LangGraph both implement multi-agent orchestration. Enterprise adoption is accelerating as teams realize complex workflows need more than a single prompt-response loop. Our coverage of OpenAI's computer access agents explores how these patterns play out in production.

How Multi-Agent Systems Work

A typical MAS architecture has three layers:

  • Orchestrator: Routes tasks, manages agent lifecycle, and aggregates results. This can be a fixed pipeline or a dynamic planner that decides which agents to invoke based on the task
  • Specialized agents: Each handles a narrow domain — code generation, web search, data analysis, file manipulation. Narrow scope means smaller context windows and more reliable outputs
  • Communication protocol: Agents exchange structured messages — task descriptions, intermediate results, and status updates. Frameworks like MCP and A2A (Agent-to-Agent) standardize these interactions

The key design tradeoff is autonomy vs. control. Fully autonomous agents can deadlock or diverge; overly controlled agents lose the parallelism benefits. Most production systems use a hierarchical model where a supervisor agent maintains global state while worker agents execute independently within defined boundaries.

  • Agentic Coding: Development workflows where AI agents autonomously write, test, and deploy code — often implemented as multi-agent systems
  • Claude Desktop: Anthropic's desktop application that enables agent-based interactions, including multi-agent coordination via tool use

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