Multi-Agent System: Definition, How It Works, and Examples

Detailed definition

A multi-agent system is several AI agents working together on the same mission rather than a single agent doing everything. IBM describes it as several agents that accomplish tasks on behalf of a user or another system, each keeping its own decision-making capability while cooperating toward a shared outcome. Google Cloud says much the same: an architecture where several agents work together on complex tasks, each with an often-specialized role, which makes it possible to break a large workflow into more manageable pieces.

The point that truly sets a multi-agent system apart from a single agent that calls other agents like tools is communication. IBM stresses this: agents interact either directly with each other, or indirectly by modifying a shared environment or memory. Without that exchange, you have a collection of isolated agents, not a system.

There's a nuance between sources on what "collaborating" actually means. IBM distinguishes two logics: a cooperative logic, where agents share a common goal (by far the dominant case in enterprise use), and a competitive or negotiation logic, inherited from academic multi-agent systems research, where agents with diverging interests negotiate with each other (a scenario cited for supply chains, for instance). Google Cloud and Microsoft, for their part, focus almost exclusively on the cooperative, enterprise-oriented version: specialized agents splitting up work toward a shared goal. That's also the angle this glossary takes.

How it works

In practice, a multi-agent system combines three ingredients.

First, role division. Each agent has an area of expertise: an agent that analyzes, an agent that drafts, an agent that verifies. This specialization keeps a single agent from carrying an overloaded system prompt, with too many tools and too much context to manage at once.

Second, communication. Agents exchange intermediate results, data, sometimes requests for clarification. According to Microsoft, this communication can follow several patterns: a sequential chain where each agent processes the previous one's output, a concurrent mode where several agents work in parallel on the same subject before their results get aggregated, or a group-discussion mode where agents debate in a shared thread before converging.

Third, coordination. That's the job of orchestration: someone, or something, has to decide which agent acts and when, and how everything gets pulled together at the end. Without coordination, several autonomous agents running in parallel mostly produce chaos.

Microsoft flags a useful point before diving into a multi-agent system: every added level of complexity (an agent with more tools, then several orchestrated agents) also brings more latency, more cost, and more ways to break. The best practice is still to start with the simplest level that meets the need, and only add agents if a single agent genuinely can't handle the variety of tasks or the required security boundaries.

A concrete example from Atako

On the Atako platform, two concrete mechanisms bring a single agent closer to multi-agent logic, without turning every agent into an overengineered mess.

The first is sub-agents. For a complex task, an Atako agent can delegate a sub-part of the work to a temporary helper sub-agent, which exists only for the duration of the sub-task. That sub-agent doesn't consume an extra slot, and all its work shows up as visible steps in the parent agent's activity timeline. You keep a single, readable view, even when several "sub-brains" are working behind the scenes.

The second is inter-agent messages. Two agents belonging to the same company can send each other messages on a dedicated channel to delegate work or share results, exactly the kind of direct communication IBM describes in its definition of multi-agent systems. This delegation isn't unlimited: delegation depth is capped, and anti-loop quotas keep two agents from endlessly bouncing the same task back and forth.

There's also an organizational dimension. Atako's Organization page lets you map out an org chart with teams and hierarchical positions, where each position can be assigned to an active agent, to a human, or left vacant. One caveat, though: assigning an agent to a position is a visual reference, not a permission. An agent's actual access (shared files, authorized integrations) depends only on its grants, not on its place in the org chart.

For a narrative example of this logic applied in a company, our article Multi-Agent Systems: When Several Agents Beat One walks through the reasoning.

Common mistakes

Confusing several agents with a multi-agent system. Creating five agents that each run on their own, never exchanging any information, doesn't make a multi-agent system. It's just five agents. Coordination and communication are the part that matters.

Multiplying agents before you actually need them. A well-equipped agent, with an autonomous agent able to handle several steps on its own, is often enough. Adding more agents adds latency and error surface with no guaranteed benefit.

Forgetting anti-loop safeguards. Two agents delegating work to each other can, with no limit, spin in circles indefinitely. A capped delegation depth and anti-loop quotas, like the ones Atako applies to its inter-agent messages, aren't a technical footnote: they're what stops a multi-agent system from running away and burning budget for nothing.

Assuming more agents means more autonomy. A poorly supervised multi-agent system isn't more reliable than a single agent, it's just harder to audit. Visibility into who did what (timeline, integration logs) stays essential, no matter how many agents are involved.

Going further

The multi-agent system is the architecture. Agent orchestration is the mechanism that runs it day to day: who decides, in what order, with what oversight. Understanding one rarely helps without understanding the other, which is why it's worth reading both definitions together before you commit to a multi-agent architecture.