Five things you can do with Sentry’s Agent Tracing

Sentry’s Agent Tracing is presented as an observability approach for debugging AI applications by connecting prompts, model calls, tool executions, handoffs, errors, latency, token use, and cost within complete conversation traces. The material outlines several practical workflows: exporting real production conversations through the Sentry CLI into evaluation datasets for tools such as Braintrust, Langfuse, promptfoo, or Arize Phoenix; scheduling automated daily triage with Claude and Sentry’s MCP connector to detect tool failures, hallucinations, loops, latency problems, and cost anomalies; monitoring Vercel AI SDK calls in Next.js through built-in tracing and conversation IDs; building dashboards and alerts for model spending; and adding Sentry as an OpenTelemetry destination without replacing an existing telemetry pipeline. Across these workflows, it recommends using deterministic checks where possible, reserving LLM judges for subjective quality, sampling successful as well as failed conversations, preventing double-counted cost data, filtering test traffic, and protecting sensitive prompts and outputs through recording controls or data scrubbing.

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