Multi-agent system: definition and setup
Definition
A multi-agent system consists of several AI agents that solve a task together, each taking a bounded part. One agent researches, another checks, a third summarises, often coordinated by a controlling instance. The appeal lies in the division of labour: clear roles, smaller steps and handovers that can be controlled individually, instead of loading everything onto a single agent.
Why the Term Matters
A single agent asked to do everything at once quickly becomes unreliable. A multi-agent system splits the task into manageable roles that can be checked and improved on purpose. For a knowledge brain this means one agent fetches evidence, another writes, a third checks against the source. Who understands the term sees when the division of labour helps and when it only adds complexity without improving the result.
When It Is Relevant
- A task naturally breaks into clearly separable substeps.
- Individual steps need different tools or rights.
- A check step should control another agent's work.
- The load should be spread across specialised roles.
When Not
When a task is cleanly solvable in one step, a system of several agents only adds friction. More agents mean more handovers, more failure points and harder traceability. A single, well-guided assistant is often the more robust choice.
Industries That Use It
- Consulting: a researcher agent and a checker agent prepare analyses with source references.
- E-commerce: separate agents for product data, stock and customer replies.
- Finance: one agent collects figures, a second checks them against the source.
- Marketing: roles for research, drafting and fact-checking in a chain.
Typical Use Cases
- A research agent delivers evidence, a writing agent turns it into a draft.
- A checker agent compares the result with the company brain before approval.
- A coordinator assigns subtasks and collects the results.
- Separate agents with their own rights each reach only their allowed system.
- A human approves at defined points in the chain.
Decision Guide
| Signal | Multi-agent system fits | Alternative |
|---|---|---|
| Task breaks into clear roles | division of labour with handovers | a single agent gets overloaded |
| Checking should be independent | a separate checker agent | self-review is weaker |
| Task solvable in one step | rather not | one assistant suffices |
| Traceability is decisive | clean cuts per role | too many agents blur the trail |
Example
A team builds a chain of three roles for analyses: one agent gathers evidence from the company brain, a second writes the draft, a third checks each claim against the source. A human approves at two points. If the check step fails, the task goes back. The example describes the method, not an asserted hit rate.
Service Connection
Divided AI agents build on a company brain, complemented by a personal AI assistant as an entry point for single roles.
Sources
This framing describes common practice around multi-agent systems. Whether several agents beat one in a given case depends on the task, the rights and the checking, and must be weighed in context.
