Sentry launched AI Agent Monitoring to help teams keep an eye on the AI/Agents in their products.
Within the same Sentry UI that developers already use to fix app issues, teams can now also see exactly how their AI features are working — or not working. They can figure out why an AI response was slow, where/why it's throwing errors, which tool or model or type of user input caused the problem, and how many resources (like tokens) were used.
All of this is deeply connected to the frontend/backend that Sentry already helps with — so if an issue is only showing up for users on a certain device/software build/region, for example, you will know. If the AI model is returning data in a way that is making the app crash but only on some dusty Android build, you will know. This would otherwise take hours and hours to track down.
With Agent Monitoring, you get a complete, interactive trace of every agent run:
- Full execution breakdowns: from the System prompts, across user input, model generation, tool usage, and final output — making it easy to see what actually happened at each step, not just where it ended up.
- Model performance details: token usage, latency, error rate — all filterable by model name and version — to surface slow, costly, or silently failing calls before they hit production users.
- Tool analytics: usage volume, durations, and failure patterns — helping you identify bottlenecks, debug tool errors, and understand where the heavy lifting is really happening.
- Error tagging and grouping: automatically groups similar failures across runs — reducing noise and letting you focus on fixing what matters most.
At launch Sentry AI Agent Monitoring will work with OpenAI, Vercel's AI SDK, and most setups that use OpenTelemetry.
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