{
  "id": "multi-agent-system",
  "code": "PTL-0140",
  "term": "Multi-Agent System",
  "aliases": [
    "multi-agent framework",
    "MAS",
    "agent team"
  ],
  "category": "agents",
  "definition": "A multi-agent system is an application in which several LLM-based agents, each with its own role, instructions, tools, or context, interact by messaging, delegating, or debating to accomplish a task.",
  "description": "Splitting work across agents can parallelize research, keep each agent's context focused, and let specialized prompts handle different steps. It also multiplies token cost and adds coordination failures, such as agents duplicating work, losing information at handoffs, or reinforcing each other's mistakes. Common structures include orchestrator-workers, handoffs between specialists, and debate.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "ai-agent",
    "orchestrator-workers",
    "handoff",
    "multi-agent-debate",
    "agent2agent-protocol"
  ],
  "introduced": null,
  "added": "2026-10-10",
  "sources": [
    {
      "title": "Large Language Model based Multi-Agents: A Survey of Progress and Challenges",
      "authors": "Guo et al.",
      "year": 2024,
      "url": "https://arxiv.org/abs/2402.01680"
    }
  ],
  "url": "https://protologue.com/t/multi-agent-system/",
  "citation": "Protologue. (2026). Multi-Agent System. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0140). https://protologue.com/t/multi-agent-system/"
}