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.
Hey Product Hunt 👋 Roman here, co-founder of Atla. We’re excited to launch Atla today: the only eval tool that helps you automatically discover the underlying issues in your AI agents.
The problem Debugging AI agents is painful. Failures hide inside long logs and are difficult to spot at scale, leaving teams to spend hours sifting through traces to understand behavior. Most monitoring tools catch individual bugs, but teams miss the recurring patterns hidden in noise.
The solution Atla automatically detects failures at the step level and clusters them into recurring patterns—so you can prioritize the issues that matter most, fix them quickly, and prevent them from reaching users.
With Atla, you can:
🧩 Detect failure patterns – Uncover recurring, high-impact failures and prioritize what matters most. 🔍 Pinpoint root causes – Dig deeper into failure patterns with step-level annotations of errors. 🕵️ Chat with your traces – Ask questions and surface patterns you’ve always suspected, backed by data. 🛠 Generate fixes – Get targeted, actionable recommendations specific enough to ship as small pull requests. ⚡ Integrate coding agents – Send fixes directly to Claude Code or Cursor for autopilot implementation. 🧪 Test changes – Track how prompt edits, model swaps, or code changes impact agent performance. ▶️ Run simulations – Replay failing steps directly in the UI to validate fixes. 🎙 Go multimodal – Extend error detection beyond text to voice agents and more.
We built Atla to save engineering teams from chasing failures one by one and to make agents more reliable at scale. Agent companies in domains like legal, sales, and productivity use Atla to save time identifying errors and to ship fixes in hours instead of weeks.
atla looks like just what I've been looking for to help with troubleshooting
I'm spending way too much time digging through agent fails, so Atla’s auto-detecting patterns is promising. That chat-with-traces idea is cool, lets me test gut feelings with data. Quick question: for a sales agent spitting out wrong pricing, does Atla suggest specific fixes, like prompt changes or code tweaks?
Debugging AI agents isn’t just about finding single bugs. It’s about spotting the patterns that keep slipping through. Atla feels like a real answer to that problem because it shows you where failures repeat and why. That’s the kind of insight that actually saves teams time.
Atla is the only eval tool that automatically surfaces what’s breaking inside AI agents step-level errors, recurring failure patterns, and root causes. Fix issues fast, before users ever notice. Smarter agents start here.
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?🤔
As someone building AI agents for enterprises - debugging AI agents is a very common and deep problem. Would definitely love to give it a try!
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.
Interesting, does this also help identify agent inefficiencies as well and suggest optimizations? Would love to automate ways to speed up my agentic workflow.
Interesting @romanengeler and Team, Just forwarded this to our CTO! Congrats on your launch :)
Congrats on the launch, Roman and the Atla team! 🚀 Your tool sounds like a game-changer for debugging AI agents. The ability to detect and cluster failure patterns should really streamline the process and help teams focus on what really matters. Excited to see how it evolves! 🎉
Super impressive, well done.
What if I'm already fully instrumented with a different system? Is there a way I can multi-home?
really like the focus on step-level visibility. most eval tools stop at surface metrics, but catching recurring failure patterns automatically is a big deal. excited to see how this helps debug agents faster without waiting on user reports.
This was a much needed tool because of increase of AI agents day by day. I just hope it works as described.
Hey Product Hunt 👋 Roman here, co-founder of Atla.
We’re excited to launch Atla today: the only eval tool that helps you automatically discover the underlying issues in your AI agents.
The problem
Debugging AI agents is painful. Failures hide inside long logs and are difficult to spot at scale, leaving teams to spend hours sifting through traces to understand behavior. Most monitoring tools catch individual bugs, but teams miss the recurring patterns hidden in noise.
The solution
Atla automatically detects failures at the step level and clusters them into recurring patterns—so you can prioritize the issues that matter most, fix them quickly, and prevent them from reaching users.
With Atla, you can:
🧩 Detect failure patterns – Uncover recurring, high-impact failures and prioritize what matters most.
🔍 Pinpoint root causes – Dig deeper into failure patterns with step-level annotations of errors.
🕵️ Chat with your traces – Ask questions and surface patterns you’ve always suspected, backed by data.
🛠 Generate fixes – Get targeted, actionable recommendations specific enough to ship as small pull requests.
⚡ Integrate coding agents – Send fixes directly to Claude Code or Cursor for autopilot implementation.
🧪 Test changes – Track how prompt edits, model swaps, or code changes impact agent performance.
▶️ Run simulations – Replay failing steps directly in the UI to validate fixes.
🎙 Go multimodal – Extend error detection beyond text to voice agents and more.
We built Atla to save engineering teams from chasing failures one by one and to make agents more reliable at scale. Agent companies in domains like legal, sales, and productivity use Atla to save time identifying errors and to ship fixes in hours instead of weeks.
Try it here:
⏯️ Interactive demo
👉 Sign-up
📒 Docs
We’d love your feedback—how do you currently debug your agents?
Also, if you made it this far, check out our *real* launch video. It’s Matrix themed.