Sentry’s Seer Agent lets developers debug production issues in natural language

Justy Exploring Next, episode 335. Sentry’s new Seer Agent is basically trying to turn, uh, production debugging into a normal conversation instead of a scavenger hunt.

Cody Yeah, and that matters right now because incidents don’t usually feel like one clean problem. It’s alerts, logs, traces, maybe a deploy, maybe not, and everybody’s jumping around trying to piece it together.

Justy Exactly. If you’re on a team shipping all the time, the pain isn’t just the bug. It’s the time lost figuring out where to even look.

Cody Seer sits inside Sentry and takes a natural-language prompt. So instead of asking someone to manually browse through issue data, you describe what’s going on and it tries to work across the context it already has.

Justy So the user story is pretty clear to me. You’re already paying for observability, you’ve already got the app in Sentry, and now you want a faster path from ‘something broke’ to ‘okay, this is probably it.’

Cody Right. And I think the interesting part is that it’s not just a chatbot pasted on top. The value is in how it can connect the prompt to real production signals. That’s the only way this is useful.

Justy Who’s the buyer here, though? I’d guess teams that already feel the sting of incident response. Smaller teams might like the idea, but if they only have a few issues a month, the adoption pressure is lower.

Cody Yeah, and the barrier is trust. If Seer says, ‘this is probably the root cause,’ people need to know whether that’s a solid lead or just a confident guess. I think that’s the whole game.

Justy And honestly, that’s true for any AI tool in dev workflows. If it saves ten minutes and points you to the right file, great. If it sends you on a weird detour, people stop using it fast.

Cody What I find clever is the shape of the problem. Debugging is already language-heavy. Engineers write the issue in Slack, in tickets, in notes, and now the tool can meet them there instead of forcing a new interface.

Justy [chuckles] Which is nice, because nobody wants another dashboard to learn at 2 a.m. when they’re already annoyed.

Cody The trade-off is obvious, though. The more the agent abstracts, the more important it becomes that it shows its work. I’d want to see what evidence it used, not just a summary.

Justy Yeah. If it’s going to win people over, it has to feel like a sharp assistant, not a mysterious one.

Cody For a weekend build, I’d start small. Take a single service’s error logs and stack traces, wire them into a local LLM workflow, and ask it to summarize one incident plus the likely code path.

Justy And if you want the solo-builder version, do it with one repo and one alert source. No big platform project. Just see if the model can actually save you time on a real mess.

Cody That’s the test, honestly. Not can it sound smart, but can it help you move faster when the app is on fire and you’re trying to stay calm.

Justy That’s our read on Seer. We’ll keep poking at what actually helps in the moment, and what just looks clever from far away. See you next time.