Picture a team of experts collaborating on a complex project: each has a specialty, they talk to one another, divide the work and correct each other. A multi-agent system (MAS) applies exactly this idea to AI: several autonomous agents — often powered by language models — interact within a shared environment to solve a problem none could handle alone.
What is an agent?
An agent is an entity able to perceive its environment, reason, then act through tools (web search, code, databases). It is often summarized by the loop:
$$ \text{Perception} \rightarrow \text{Reasoning} \rightarrow \text{Action} \rightarrow \text{Observation} $$
In a MAS, we multiply these loops and make them communicate. Each agent can take on a specific role: planner, executor, critic, verifier.
Coordination architectures
| Architecture | Principle | Typical use |
|---|---|---|
| Orchestrator | A lead agent delegates to sub-agents | Research pipelines |
| Hierarchical | Managers and cascading sub-teams | Long, structured projects |
| Decentralized | Agents negotiate as peers | Markets, auctions, simulations |
| Debate | Agents argue, then vote | Answer reliability |
Why it is powerful
Specialization lowers each agent's cognitive load, and cross-criticism limits errors: a verifier agent can catch another's hallucination. This is the principle behind modern frameworks (AutoGen, CrewAI, LangGraph).
Beware, though: adding agents also multiplies cost, latency and the risk of error propagation.
Where a lone agent thinks, a multi-agent system deliberates — cooperation becomes a form of intelligence in its own right.