Multi-agent systems are coordinated AI architectures where specialist agents handle sub-tasks under supervisor orchestration. Built for enterprises with workflows too complex for a single agent, they decompose goals, route work, verify outputs, and enforce loop and budget limits — with traces that make failures debuggable instead of mysterious.
Architectures that hold up outside the demo
Supervisor Orchestration
A coordinating agent that decomposes the goal, routes each sub-task to the right specialist, checks the result against the original intent, and decides whether to accept, retry or escalate. Control flow you can read, not emergent chaos.
Specialist Agent Design
Each agent gets a narrow remit, its own tools, and its own evaluation set. Narrow agents are dramatically easier to test and improve than one generalist trying to do everything with a prompt the length of a novel.
Shared State & Memory
A single inspectable state object rather than agents passing prose to each other. You can see exactly what each agent knew when it made its decision — which is the difference between debugging and guessing.
Loop & Budget Guards
Hard limits on iterations, tool calls, wall-clock time and spend per run. Agent systems fail by looping expensively, so the guard rails are part of the architecture rather than something bolted on after the first surprise invoice.
Critic & Verifier Agents
Adversarial agents whose job is to attack the output before you see it — checking for unsupported claims, missed constraints and policy violations. Independent verification catches what a self-review will always miss.
Full-Trace Observability
Every message, tool call, retry and handoff captured and replayable. When a run goes wrong you step through it like a stack trace instead of re-running it hopefully and getting a different answer.
Multi-agent, engineered rather than improvised
Justify the Topology
First we check whether you need multiple agents at all. A single well-tooled agent is cheaper, faster and easier to debug, and it is the right answer more often than the industry admits. We only go multi-agent when the task genuinely decomposes.
Contract Definition
Each agent gets a typed input and output contract. Agents communicating in free text is the single biggest source of multi-agent flakiness — structured contracts turn a whole class of failures into validation errors you can catch.
Per-Agent Evaluation
Every specialist gets its own test set and passes independently before it is wired into the system. Debugging a twelve-agent system where no individual agent was ever verified is not an experience we recommend.
System-Level Evals
End-to-end evaluation on real tasks with scored outcomes, run on every change. This is what tells you whether the latest prompt tweak actually improved the system or just moved the failure somewhere else.
Production Hardening
Timeouts, retries with backoff, circuit breakers, graceful degradation to a simpler path, and cost alerting. A multi-agent system without these is a prototype, whatever the demo looked like.
Multi-Agent Systems
FAQ.
Common questions about multi-agent system development — when to use them, frameworks, loop and cost control, debugging and deployment.
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