Multi-agent system
An architecture in which multiple specialized AI agents coordinate, each handling part of a larger task, rather than a single agent doing everything.
In more detail
A multi-agent system divides a problem among several agents, each with a focused role, that pass work between them. One might plan, another research, another draft, another check. The idea mirrors how a team of specialists can outperform one generalist on a complex task, provided the coordination between them is well managed.
More agents is not automatically better. Each hand-off is a place for information to be lost or a task to drift, and coordination adds overhead. Multi-agent designs earn their complexity when a task genuinely decomposes into distinct roles; for simpler work a single well-orchestrated agent is often clearer and more reliable.
Where this shows up at Ceven
Where a task benefits from specialization, Ceven can coordinate multiple steps and roles within one workflow rather than leaving the customer to wire agents together by hand. The orchestration, the hand-offs, the approval gates, and the audit trail are handled by the platform, so the customer experiences one workflow toward an outcome rather than a loose collection of agents.