Multi-Agent System
An architecture where multiple specialised AI agents collaborate, each with its own role, tools, and scope.
What is Multi-Agent System?
A multi-agent system divides complex work across agents: one retrieves, one reasons, one validates, one acts. This decomposition improves reliability and auditability — each agent has a narrow scope and can be evaluated independently. See Multi-Agent Systems and Secure Multi-Agent Networks.
What is an example of Multi-Agent System?
A software-maintenance system has one agent reproduce an issue, another propose a patch, a third review security impact, and a coordinator decide whether tests and approvals allow the change to proceed.
How is Multi-Agent System different from related concepts?
A workflow can call one agent multiple times without becoming a multi-agent system. The defining feature is multiple independently configured or acting agents with explicit interactions.
Why it matters for on-premise & regulated AI
Multi-agent systems raise a governance question single agents do not: who is accountable when agents delegate to each other? In regulated environments the answer must be reconstructable from logs — which agent acted, on whose request, with what data. Hosting the whole system on-premises gives you one coherent, exportable trace across all agents instead of fragments scattered across vendor clouds.
What should enterprises evaluate for Multi-Agent System?
- Specify each agent’s role, authority, data access, success criteria, and allowed communication paths.
- Protect against loops, duplicated work, compromised peer messages, and cascading low-confidence output.
- Compare quality, latency, cost, and operability against a simpler single-agent or deterministic workflow baseline.
Read the full guide: Multi-Agent System — in-depth article →
Related terms
Putting Multi-Agent System to work?
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