Building production agents: the observability layer

AI agents can produce plausible but incorrect results without errors because their non-deterministic, multi-step workflows may involve faulty tool arguments, poor tool selection, abandoned context, looping, or degraded reasoning that traditional application monitoring does not reveal. Effective agent observability therefore requires correlated session-level traces that capture planning, model calls, tool executions, backend activity, latency, token usage, costs, errors, and multi-agent handoffs on a shared timeline, ideally using portable OpenTelemetry standards. The piece argues that conventional APM, manual logs, and LLM-focused tracing tools offer only partial visibility, particularly when database and application work must be connected to agent behavior. It presents Pydantic Logfire as an OpenTelemetry-based platform that can instrument Pydantic AI agents with minimal setup, providing conversation replay, tool inspection, cost tracking, full-stack traces, SQL-accessible telemetry, MCP support, and integration with evaluation datasets. It concludes that observability should be established early in production agent development so incidents can become reproducible tests, costs can be monitored per step, and agent quality can improve through measurable feedback.

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