A multi-agent system is a system in which multiple autonomous or semi-autonomous agents interact within a shared environment. They may cooperate on a common goal, divide a complex problem into specialized roles, negotiate responsibilities or, in some settings, compete.
The concept predates modern large language models, but it has become important again because AI agents can now use language models, tools and external services. Instead of asking one agent to do everything, a multi-agent architecture assigns different responsibilities to different agents and defines how their work is coordinated.
How a multi-agent system works
A typical system has three layers: individual agents, a communication or orchestration mechanism, and shared task state. Each agent may have its own instructions, tools and memory. The orchestration layer decides which agent should act, how results are passed between agents and when the overall task is finished.
Google Cloud defines multi-agent systems as networks of autonomous, interacting computational entities that can collaborate, coordinate or compete to achieve goals. Modern implementations often add language models as the reasoning component inside each agent.
Why use multiple agents instead of one?
Specialization is the main argument. One agent can be optimized for planning, another for searching evidence, another for writing code and another for reviewing output. Separating responsibilities can make prompts and tool permissions easier to reason about than one very broad agent with access to everything.
However, specialization is not automatically an improvement. Every additional agent adds communication, latency, cost and another place where incorrect assumptions can spread.
Common multi-agent architectures
Coordinator and specialists
A central coordinator receives the goal, delegates subtasks to specialists and combines their results. This is easy to understand and can enforce a clear workflow, but the coordinator can become a bottleneck.
Peer-to-peer collaboration
Agents communicate more directly and decide among themselves how to divide work. This can be flexible, but it is harder to predict and debug because control is distributed.
Planner, executor and reviewer
One agent creates a plan, one or more agents execute it, and another agent checks the result against requirements. This pattern can add useful separation of duties, especially when the reviewer has different tools or constraints.
Hierarchical agent teams
Large workflows can use layers of coordinators and specialists. Hierarchies help organize many capabilities but also increase the need for observability and clearly defined authority.
Example: multi-agent research
A research workflow might assign one agent to literature search, another to data analysis, a third to challenge assumptions and a fourth to synthesize a report. In scientific work, these roles are only useful if the system preserves provenance and makes it possible to inspect how each conclusion was reached.
An AI scientist can use a multi-agent architecture, but it does not have to. “AI scientist” describes the research function, while “multi-agent system” describes one possible architecture.
Example: software engineering
A planner can break a feature into tasks, an implementation agent can modify code, a testing agent can run checks and a reviewer can inspect whether requirements were met. The team still needs a shared representation of repository state and rules to prevent agents from overwriting one another’s work.
Communication and shared state
Agents need a protocol for messages and a model of state. Shared state might include task progress, files, database records or a structured plan. If different agents hold inconsistent views of the same state, they can duplicate work or make contradictory changes.
Security gets harder with more agents
Each agent may have a different identity and different permissions. A coordinator that can delegate authority must not accidentally grant a specialist more access than necessary. NIST’s AI Agent Standards Initiative explicitly includes research into agent authentication and identity infrastructure for secure human-agent and multi-agent interactions.
Debugging and evaluation
When a result is wrong, developers need to know which agent made which decision, what data it saw and what tool it used. Logs should capture messages, tool calls, permissions and important state transitions. Without that trace, a multi-agent system can become harder to diagnose than a single-agent workflow.
When a single agent is better
Use one agent when one clear toolset and one goal are enough. A single agent is usually cheaper, easier to secure and easier to test. Multi-agent architecture is justified when specialization or parallel work provides a measurable benefit that outweighs coordination overhead.
