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.
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Interesting, does this also help identify agent inefficiencies as well and suggest optimizations? Would love to automate ways to speed up my agentic workflow.
@tarun_pasumarthi we've had many users ask for this! Currently our critic focusses on catching mis-steps, but we're actively thinking about how to find inefficiencies as well by "backward passing" through the entire trace.
So for instance if an agent arrived at an answer to a simple question but used 20 steps of reasoning to do so - we wouldn't flag this currently walking forward through the trace, but we're exploring whether it becomes clearer looking back!
Ah interesting idea! Would be cool to see the backward pass method working.
Looks good, how does Atla define an error?
In my mind, the agent run multiple steps and have some results, but sometimes the result doesn't satisfy the needs which may not an error but need more rounds input.
@new_user___1342025547691234062bac1 great q! We try to catch any steps of the agent that deviate from its instructions/request/context so far, for e.g. if the agent ran several reasoning steps that were all logically sound, grounded, followed the brief etc. they would pass.
On the flip side, if the agent failed to ask the user for some critical piece of information (as specified by its instructions) and eventually failed because of this, we would flag this. We're constantly working on making this step-level critic's annotations more precise!
Thanks @hannah_cooper4! Yeah absolutely, for each pattern that we find, we suggest small-PR sized fixes (e.g. to the system prompt, tool descriptions etc), and we have a "copy for AI" button so you can quickly prompt your coding agent to implement those suggested fixes
I know first hand how hard this is, so I'm very excited to see a working solution to the agent error problem. Super excited to try this out.
Curious to know what the roadmap is looking like for the foreseeable future if you could share!
Great question! A few things on the roadmap we’re excited about:
Dev workflow: custom evaluation metrics and patterns inside the Comparison feature, plus tighter git integration to auto-version experiments
Simulations: smoother UX so you can quickly test prompt/tool iterations in the UI and deploy the best performer
Coding agent integration: better interfaces so tools like Cursor or Claude Code can tackle failure patterns on auto-pilot, just like working through Jira tickets
... and plenty more in the pipeline!
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.
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!
@thelemonbot Pattern king
Congratulations on your Product Hunt launch! Atla looks like a powerful tool for debugging and improving AI agents. What’s your vision for how Atla will evolve to address new types of AI failures in the future?🤔
@lvyanghuang thank you! and great q - I think as agents get more powerful & tackle more complex tasks, we envision our critics keeping up, and getting better at flagging precise errors in long-winded and complex traces!