Multi-Agent System
A multi-agent system, abbreviated MAS in the AI literature, is the architectural pattern where multiple
LLM-powered agents — each with specialized roles, tools, and capabilities — collaborate to solve problems that exceed any single agent's effectiveness. The pattern has roots in classical AI (Russell and Norvig, distributed AI literature from the 1990s) but has exploded in 2023-2025 with frameworks that make agent coordination practical: AutoGen (Microsoft, agent conversation patterns), CrewAI (role-based agent teams), LangGraph (LangChain's graph-structured agent workflows), MetaGPT (software-development simulation), Camel (role-playing dialogue), and the rapidly evolving ecosystem around OpenAI's Swarm and Anthropic's Claude with native tool-use orchestration. The taxonomy of multi-agent architectures: hierarchical (a manager or supervisor agent dispatches to specialist agents), peer-to-peer (agents collaborate as equals via shared workspaces), assembly-line (agents pass work sequentially), and emergent (agents communicate freely with minimal structure). Common roles in production systems include a planner (decomposes the problem), executors (do specialized work — research, code, draft, review), a critic (evaluates intermediate outputs), a fact-checker (verifies citations), and a coordinator (manages state and handoffs). The trade-offs are real: multi-agent systems improve performance on complex tasks (code generation, research, complex workflows) but multiply latency, cost, and unpredictability — every agent call is a separate LLM call with its own tokens, errors compound across handoffs, and emergent behaviors can be hard to debug. The hot research area as of 2025 is multi-agent reasoning: how to allocate effort across agents, prevent collusion or echo-chamber failure modes, and produce auditable traces. AI governance teams treat multi-agent systems as compound applications — every agent gets its own model card, prompt, evaluation, and audit trail, and the orchestration layer logs every handoff so post-hoc analysis can reconstruct what happened.
Multi-agent workflows from 25 years of workflow discipline: Centralpoint has orchestrated enterprise content workflows — author, review, approve, publish, audit — for 25 years across multi-role environments. Multi-agent AI orchestration is the same workflow discipline applied to a new actor type. Orchestration runs on-premise, tokens meter per skill (and per agent), and multi-agent chatbots deploy through one line of JavaScript.
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