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AI & Agents

Multi-Agent System

A multi-agent system is an architecture where several specialised AI agents work on one goal, typically coordinated by a supervisor agent that decomposes the task, routes each part to the right specialist, and checks results before accepting them. Each agent has its own narrow remit, tools and evaluation set.

Multi-agent architectures solve problems a single agent cannot, and they multiply the ways a system can fail. Agents passing free-form prose to each other is the most common source of flakiness; typed contracts between agents turn a whole class of failures into validation errors you can catch. Hard limits on iterations, tool calls and spend per run are architectural requirements rather than later additions, because the characteristic failure is an expensive loop.

The honest starting question is whether multiple agents are needed at all. A single well-tooled agent is cheaper, faster and far easier to debug, and it is the right answer more often than the industry admits. Multi-agent designs earn their cost when the task genuinely decomposes into sub-tasks needing different tools, different context or different judgement — and when those sub-tasks can be verified independently.

Codazz builds this in production — Multi-Agent Systems.

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Multi-Agent System
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Common questions about multi-agent system.

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When the task splits into sub-tasks that need different tools, different context or different judgement, and each can be verified on its own. If a single agent with a good tool set handles the job, use that — it costs less, runs faster and is far easier to debug. Test the single-agent baseline before designing anything more complex.

With limits enforced outside the model: a maximum number of iterations per run, a cap on tool calls, wall-clock timeouts, and a spend ceiling per run and per day. Breaching a limit should terminate the run and raise an alert rather than degrading quietly. Cost per successful outcome should also be monitored, because a system that stays under its cap while retrying constantly is still failing.