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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Promoted
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.
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.
@yspfilm This is amazing!
Finally someone has crafted a tool that evals Agents... too many agents nowadays and I believe Atla could be a stress testing tool for them... How does it cater to different scenarios and biz logics?
Thank you @cruise_chen! Super important to stress test agents before sending them into the wild.
We've benchmarked our granular LLMJ annotator on many scenarios (customer support, coding agents, browsing etc) but the real adaptiveness comes from aggregating these into failure patterns tailored to each individual agent - rather than generic eval criteria, you see the specific ways in which your agent is misbehaving.
We're already working on the next steps of customizability - which is letting users dynamically shape patterns over time to make them their own, and understanding how different patterns influence specific business metrics of interest!
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.
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.
@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.
Super impressive, well done.
What if I'm already fully instrumented with a different system? Is there a way I can multi-home?
@mbanerjeepalmer Yes you can! We've seen people use both Atla + Langfuse. Which other observability system do you use?
@kaikaidai Grafana for one project, Logfire for another, and good ol lines upon lines of JSON for others
Congratulations on the launch! 🚀 I’m building AI agents for business workflows, and error detection is always tough. Does Atla only look at LLM outputs, or can it diagnose issues across the whole agent process—including code and APIs? How customizable is the error tracking for unique workflows? Would love to hear if teams use Atla for improving non-LLM agents too.
@sneh_shah this is a great question! We currently focus on LLM outputs, which include the LLM tool calls, i.e. the tool call arguments, and also handoffs to other agents. Thus, we assume that the tool outputs are correct, and we leave the intricacies of the tool and tool error handling to the developer, though, we do pick up on how the agent reacts to tool outputs. Systematic issues across agent processes are then highlighted in common failure patterns.
The error tracking is automatically customised to your system message and tool information - thus, we measure how well the agent follows the policy and how well it completes the task that you have specified, rather than have you repeat this information in the evaluation. There is further customisability where individual metrics can be tracked in our custom metrics suite.