Atla is the only eval tool that helps you automatically discover the underlying issues in your AI agents. Understand step-level errors, prioritize recurring failure patterns, and fix issues fast–before your users ever notice.
Congrats on the launch! Early adopter here 👴 If you like saving time, getting things done and, you know, care about the quality of your product, then this is probably for you 😄
Massively proud of the whole @Atla team for getting us here - it's been a labor of love, and we're finally out there ❤️
We spend all our time thinking about how to diagnose agent failures better, faster & smarter - and we've found the most reliable route to be focussing on recurring failure patterns (to cut through the noise), while keeping an eye out for new ones (to stay on-policy).
I think we've built something pretty cool that attempts to do that, but more importantly we're eager to learn continuously from feedback and make our eval tools better - so that people can make their agents better. Give us a try & let us know what you think!
Debugging AI agents has always felt like chasing shadows. Not anymore.
What I love most:
Step-level visibility
Pattern clustering
Actionable fixes + integrations with tools like Claude Code make it feel like an engineer is already drafting the PR for you.
And the ability to chat with traces is a total gamechanger. finally a way to ask “what’s really happening here?” and get a real answer, backed by data.
Super excited to see where the roadmap takes it. Congrats again, Roman, Jackson, and team! this is going to be a must-have for anyone building at the frontier of AI.
@kkonrad Thanks for your support Konrad 🥜! Happy to see you highlight the chat with traces feature, which the team made a big push to ship for this launch! We want agent builders to not only see critical failures quickly, but also dig deeper into issues that matter most for their own users.
When you chat with traces, you get an answer and a list of relevant traces where that issue is occurring. Excited for people to use this and more in Atla.
Nice! Really enjoyed the demo. It seems like it can easily surface the cause of errors that took us a long time to debug previously.
Also liked the compare feature as it seems to uncover the different failure modes of models and see the improvements / degradation between experiments.
Excited to implement it and see if then just handing the quick fix to CC will solve the errors. That would be fantastic.
Exactly — the core value is in automatically surfacing failure patterns and highlighting what matters, so you don’t drown in noisy logs.
Early tests show Claude Code can already implement fixes quite well. We’re working on making it more reliable by detecting precise failure patterns, which lets coding agents apply targeted fixes and avoid regressions. That way they can iterate quickly through errors.
Instruct
Awesome launch, well done to the team! Definitely need try this out soon :)
Atla
Thanks Alfie!
Nayla
Much needed product!!! Can this tool handle sub-agents?
Atla
It sure can! We always perform our evals from the perspective & with the context of the active (sub)agent
Fieldy
Congrats on the launch! Early adopter here 👴 If you like saving time, getting things done and, you know, care about the quality of your product, then this is probably for you 😄
Atla
@karolis_mariunas Thanks Karolis! 🙏 Really appreciate you being such an engaged early user - your feedback has been invaluable in shaping the product
Atla
Massively proud of the whole @Atla team for getting us here - it's been a labor of love, and we're finally out there ❤️
We spend all our time thinking about how to diagnose agent failures better, faster & smarter - and we've found the most reliable route to be focussing on recurring failure patterns (to cut through the noise), while keeping an eye out for new ones (to stay on-policy).
I think we've built something pretty cool that attempts to do that, but more importantly we're eager to learn continuously from feedback and make our eval tools better - so that people can make their agents better. Give us a try & let us know what you think!
Atla
@thelemonbot Pattern king
Knit – Your Virtual Meeting Place
Big congrats to the Atla team on launch!!
Debugging AI agents has always felt like chasing shadows. Not anymore.
What I love most:
Step-level visibility
Pattern clustering
Actionable fixes + integrations with tools like Claude Code make it feel like an engineer is already drafting the PR for you.
And the ability to chat with traces is a total gamechanger. finally a way to ask “what’s really happening here?” and get a real answer, backed by data.
Super excited to see where the roadmap takes it. Congrats again, Roman, Jackson, and team! this is going to be a must-have for anyone building at the frontier of AI.
Atla
@kkonrad Thanks for your support Konrad 🥜! Happy to see you highlight the chat with traces feature, which the team made a big push to ship for this launch! We want agent builders to not only see critical failures quickly, but also dig deeper into issues that matter most for their own users.
When you chat with traces, you get an answer and a list of relevant traces where that issue is occurring. Excited for people to use this and more in Atla.
First Words - Multilingua
Nice! Really enjoyed the demo. It seems like it can easily surface the cause of errors that took us a long time to debug previously.
Also liked the compare feature as it seems to uncover the different failure modes of models and see the improvements / degradation between experiments.
Excited to implement it and see if then just handing the quick fix to CC will solve the errors. That would be fantastic.
Atla
Exactly — the core value is in automatically surfacing failure patterns and highlighting what matters, so you don’t drown in noisy logs.
Early tests show Claude Code can already implement fixes quite well. We’re working on making it more reliable by detecting precise failure patterns, which lets coding agents apply targeted fixes and avoid regressions. That way they can iterate quickly through errors.
Instruct
Congrats! Atla is a much needed product - and it's awesome to see this launch.
Atla
Thanks Matt, appreciate the kind words!