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.
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.