Sentry on The Pragmatic Engineer

Transcript

I'd also like to mention our season sponsor, Sentry. AI agents don't have a feedback loop from production observability logs, but should they have it? I mean, we probably don't want to build a system where after each production error, AI agents automatically push out a fix into production without supervision. That would be a recipe for disaster. But what if we did something more modest? When a production error fires, an agent investigates this with context from Sentry. Sentry already has all the context in the error after all. Sentry MCP is one way to plug in Sentry to agents that support the model context protocol. Cloud Code, Cursor, Codecs, Versus Code, Copilot, they all do. After you hook up the MCP server, you can do some very useful things. For example, you could do this. When an already resolved Sentry issue resurfaces, you can kick off a cursor agent to investigate the regression, read their relevant code, and open a PR with a suggested fix. There's a little work involved to get all of this going. You need to connect Sentry to your code repository, add Sentry MCP to Cursor, define the instructions for Cursor's agent to investigate, configure the trigger that launches the automation, and test that it all works. But once you have it up and running, you can get regressions fixed faster whilst you're reviewing every and all fixes. This feels like a sensible and helpful use of AI and CP and Sentry to me. Check out Sentry at sentry.io/pragmatic and start monitoring and fixing regressions today.