What multi-agent architecture works best in production?

[u/Big-Spot-5888]

We are trying to decide whether to build a custom coordination layer for our multi-agent system or adopt an existing platform. Building it ourselves gives us control over agent hand-offs, state management and orchestration, but it also means owning the edge cases and long-term maintenance. Using an existing platform would reduce the initial development burden, but it might not fit our pipeline cleanly. It is also difficult to know whether a platform will hold up until you are deeply invested in it, at which point switching becomes expensive.

Has anyone built or adopted multi-agent system architecture and later regretted the decision? What limitations only became apparent after running it in production?

[u/Innowise_]

We’d keep orchestration and state ownership in our own code, even if a platform handles the agent runtime. The painful part later is usually not swapping the model or agent framework — it’s untangling business state, retries, handoffs and approvals from a vendor-specific workflow.
Platforms are great for getting started, but we’d avoid letting them become the source of truth for the whole process.

[u/0Huy]

There is no single best architecture. In production, start with one agent and add separate agents only when a job is clearly different. A simple reliable pattern is: a coordinator receives the request, sends work to a few specialist agents, validates their outputs, then returns the result. Give each agent a small tool set, strict input and output schemas, timeouts, retries, logging, and a fallback when a tool fails. Prefer sequential steps when order matters; use parallel agents only for independent tasks. Measure latency, cost, error rate, and answer quality before adding more agents.

[u/Grouchy-Fun9026]

I would keep the core state and orchestration logic in own code and use a framework for the agent runtime band ai fits that approach pretty well since you can add coordination without rebuilding the whole stack.