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.
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
Gratifying to see teams understand their agents' failures, prioritize high-impact issues, and ship fixes in days instead of weeks with Atla. Excited to help more teams with this!!
Congrats on the launch! It's a super interesting product, especially with the clustering of recurring failure patterns. Debugging agents can feel like chasing ghosts in giant logs, so surfacing the systemic issues instead of one-offs feels like a big unlock.
Have you seen teams use Atla more for proactive QA before launch or for post-deployment firefighting?
Thanks for the kind words—really glad to hear you’re enjoying it!
We typically see teams using it quite proactively; testing out a new big feature, making some quick prompt improvements or just sparking ideas for what the next big launch should be.
Thank you@yuncheng - we're indeed excited to help people (less painfully) evaluate their agents, we know it can be done!
Report
@yuncheng@thelemonbot so needed right now -- startup space is absolutely saturated with "AI" products that barely function. IMO we're going to see a massive paring down soon; people and investors are growing tired of advertised features that don't work.
Atla
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.
Asteroid
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!
Atla
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!
Atla
Gratifying to see teams understand their agents' failures, prioritize high-impact issues, and ship fixes in days instead of weeks with Atla. Excited to help more teams with this!!
Check out our live demo for a play around: https://demo.atla-ai.com/app/deep-search
Check out our secret launch video for a laugh:
Looking forward to your feedback! And we'll be here for questions.
Selene by Atla
@yspfilm This is amazing!
Congrats on the launch! It's a super interesting product, especially with the clustering of recurring failure patterns. Debugging agents can feel like chasing ghosts in giant logs, so surfacing the systemic issues instead of one-offs feels like a big unlock.
Have you seen teams use Atla more for proactive QA before launch or for post-deployment firefighting?
Atla
Thanks for the kind words—really glad to hear you’re enjoying it!
We typically see teams using it quite proactively; testing out a new big feature, making some quick prompt improvements or just sparking ideas for what the next big launch should be.
X-Design
Just upvoted—this deserves attention. It feels like the team deeply cares about solving real user problems rather than chasing hype.
Atla
Thank you@yuncheng - we're indeed excited to help people (less painfully) evaluate their agents, we know it can be done!
@yuncheng @thelemonbot so needed right now -- startup space is absolutely saturated with "AI" products that barely function. IMO we're going to see a massive paring down soon; people and investors are growing tired of advertised features that don't work.
Atla
Had a ton of fun building this with the team and hope you enjoy it!
Anything you like, let me know; anything you don't like, let @kaikaidai know.
Atla
Thanks Jorge! It was a big push and we're all very excited where we got in the end!