Multi-agent MCP systems make a hard question unavoidable: when an agent produces an answer by calling several tools across several servers, when do you trust it, when do you retry, and when do you escalate to a human? Most setups answer with static rules that don't improve.

I'll demonstrate a confidence-gated coordination pattern for this, built as a working reference over real MCP servers. The agent routes each result by a confidence-and-correction signal: a confident, human-confirmed answer reinforces that path; a corrected answer triggers a different one — retry, reroute, or adapt. Reinforce when right, retrain when wrong — a routing-and-failure-handling policy that sits over your MCP tool calls.

I'll walk the reference end to end on one concrete case — a customer-care agent deciding a billing credit across account, policy, and issuance servers — showing where the gate lives in the MCP topology, how state flows across servers, and how retries and human escalation are handled. I'll be honest about its limits and where it breaks.

You'll leave with a coordination pattern, and a working reference you can adapt into your own multi-server MCP architecture.