Multi-agent system

A multi-agent system is a set of agents with distinct jobs working on one task. A common shape is planner, worker and reviewer: one agent breaks the goal into steps, others execute each step with their own narrow tools, and one checks the output against the goal and the rules before anything is committed. Each agent has a small, focused prompt and a small tool set, which makes it easier to test and harder to misuse.

The reason to split rather than build one large agent is the same reason teams split work between people: context, specialisation and checks. A single agent with twenty tools and a long prompt drifts and becomes hard to evaluate. Several agents with three tools each, and a reviewer that can reject, are easier to reason about, and each can be evaluated on its own golden set.

It is not automatically better. Multi-agent designs add latency, cost and coordination failures, and a task that one agent handles well should stay with one agent. The signal that you need several is when one agent's prompt keeps growing, when different steps need different permissions, or when a review step must be independent of the agent that did the work.

• CTO / Head of Engineering: it is the design that keeps agent systems testable as scope grows; each agent gets its own evals and permissions.
• CISO: separating a reviewer from a worker, and giving each agent only the tools it needs, limits what any one compromised prompt can do.
• Operations head: workflows with several handovers map naturally to several agents, mirroring how the team already splits the work.
• CFO: more agents mean more model calls; the design should be justified by scope or safety, not fashion.

Single agents scale poorly. As tasks get longer and tool sets get bigger, one model in one loop makes more mistakes and becomes impossible to evaluate piece by piece. Multi-agent systems exist to give each part of a workflow a small, testable agent with the least access it needs, and to put an independent check before actions are committed. The failure they prevent is the monolithic agent that nobody can safely change. The trade-off is cost and latency from extra model calls, and new failure modes in coordination, so the split must be earned by the task rather than applied by default.

• A KYC pipeline where one agent extracts fields, another verifies them against sources, and a reviewer flags mismatches for a human.
• A SaaS support system where a triage agent classifies, specialist agents handle billing or technical issues, and a reviewer checks tone and policy.
• An insurance claims-triage system with intake, fraud-signal and routing agents feeding a single adjuster queue.
• A logistics control tower with monitoring, customer-contact and rebooking agents under one orchestrator.
• A course-content system where a drafting agent writes, a curriculum agent checks alignment, and an editor agent enforces style before human review.

Technique / practiceAn architectural technique. Eazyware's multi-agent systems service designs the split, the orchestration and the per-agent evals, and will recommend a single agent when that is the simpler, cheaper answer for the task.

Eazyware service that covers it: Multi-Agent Systems & Workflow Orchestration. Starting prices are on the pricing page.