What I Learned Building Multi-Agent Systems

Building multi-agent systems in production taught me things no whiteboard session ever could. Here's what I've learned after a year of shipping agent orchestration infrastructure.

Agents fail. Plan for it.

The first thing you learn is that agents are unreliable by nature. They hallucinate, they timeout, they produce outputs that don't match your schema. The difference between a demo and a production system is how gracefully you handle these failures.

We built a retry-with-backoff layer that treats agent calls like any other distributed RPC. If an agent fails, the orchestrator knows how to retry, fall back to a simpler model, or gracefully degrade the entire workflow.

Observability is non-negotiable

You can't debug a multi-agent system with print statements. We instrumented every agent call with structured logs, traces, and cost tracking from day one. When something goes wrong at 3am, you need to be able to trace the exact chain of agent decisions that led to the failure.

The orchestrator is the product

Most teams focus on the agents themselves — prompt engineering, model selection, tool use. But the orchestrator is where the real complexity lives. It decides which agents to invoke, how to compose their outputs, when to parallelize vs. sequence, and how to handle partial failures.

The best agent system is one where you can swap any individual agent without changing the orchestration logic.

What I'd do differently

If I were starting over, I'd invest more in schema validation between agents. We spent too much time debugging issues that boiled down to one agent producing slightly different output than the next agent expected. A strict contract layer between agents would have saved us months.

I'd also start with cost tracking from day one. Agent costs compound fast when you have chains of 4-5 models running per request. Without visibility into per-request costs, you're flying blind.