How to Debug AI Agents in Production Using MCP Observability Tools

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AI agents fail silently — taking extra turns, calling wrong tools, or returning subtly incorrect answers without throwing exceptions or triggering standard alerts. Part 10 of a 15-part series on Model Context Protocol (MCP) explains how to build full observability by linking spans across the host, model, and all MCP servers under a single root trace per agent run. Structured logs capturing tool name, server, tenant, latency, and outcome are correlated by trace ID, while payloads are redacted to prevent observability systems from becoming unsecured copies of customer data. MCP-specific metrics — including per-tool latency, error rates, turns per run, and token cost — replace generic request-per-second dashboards to surface regressions at the tool and tenant level. An append-only audit log reconstructs the full sequence of tool calls, results, and guardrail decisions, enabling incident triage in minutes rather than hours.

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