A multi-agent system is a set of AI agents, each with its own role, tools and limits, that work together on tasks too broad for one agent. One gathers documents, another checks them, a third drafts the outcome, and some form of coordination ties their work together. Splitting work this way makes each agent narrower and easier to test, but adds handoffs that need control.
Why build a multi-agent system instead of one agent?
Because narrow agents are easier to prove. An agent that only extracts data from invoices can be tested thoroughly on invoices. An agent that extracts data, matches it to orders, chases suppliers and prepares payments has four capabilities to test at once, and a failure in one affects all four.
Separation also helps with control. The agent that prepares a payment can be kept apart from the agent that talks to suppliers, so a supplier's email cannot reach the payment step directly.
Where does risk enter a multi-agent system?
At the handoffs. Three problems recur:
- Authority leaks. An agent cleared only to read passes a task to one cleared to execute, and the action happens without anyone deciding the first agent's request deserved it.
- Provenance is lost. A fact that arrived as an external claim is summarised by one agent and passed on as plain text, losing its class along the way.
- Plans drift. Long chains of agents amplify small errors. Whether agents can plan multi-step tasks reliably depends heavily on how the steps are checked.
Each is solved outside the agents: every action is checked against the agent that takes it, facts keep their class through every handoff, and each step's result is checked before the next starts.
What does a multi-agent system look like in an example?
A wealth manager's annual review process uses four agents. One collects updated client information. One checks it against the client file and flags changes. One drafts the suitability review. One schedules the adviser meeting. The drafting agent may not see the scheduling tools; the scheduling agent may not see financial data. When the collecting agent receives a client's note saying "I'd like to move everything to cash", that note is passed on as declared, and the drafting agent can include it but cannot act on it. The adviser decides.
How is a multi-agent system different from a single agent with many tools?
A single agent with many tools makes all decisions in one context, under one set of instructions, with one certificate covering everything. A multi-agent system divides decisions between agents with separate instructions, separate tools and separate certificates. The first is simpler to build. The second is easier to bound and to prove, especially in regulated work where some steps carry far more risk than others.
If any agent in the system makes decisions that fall in a high-risk category under the EU AI Act, such as credit scoring of individuals, the whole system's use in that context needs the matching controls. See what a high-risk AI system is.
Where Pryme fits
Pryme checks every action from every agent against that agent's own minimum chain, so authority does not pass along handoffs. It can run where your data lives; see running an agentic operating system on your own infrastructure and the governed agentic operating system, explained.
Questions people ask
How many agents is too many?
When adding an agent no longer makes any agent easier to prove, stop. Each handoff is a place for errors and leaks, so split only where it makes testing and control simpler.
Do agents in a multi-agent system share memory?
Sometimes. Shared memory is convenient but needs care, because one agent's note can become another agent's fact. Every shared entry should carry where it came from.