Hey everyone! Roman here, cofounder of Atla.
Atla helps teams building AI agents find and fix recurring failures. Instead of just surfacing endless traces, Atla automatically identifies the patterns behind failures and suggests targeted fixes.
Ask: Building agents? Try us at atla-ai.com to find your agent’s most critical issues and ship improvements in hours, not days.
https://youtu.be/LtvKBJKPxKE?feature=shared
Agents are complex black boxes, chaining together plans, tool calls, and agent-to-agent interactions. For this reason:
We’ve spoken to over 100 teams who experience similar pains of digging through thousands of traces without clear signal, and fixing one issue only to have another pop up. This slows down shipping and erodes trust in the system.
Atla turns all that noise into actionable insights:
The result: instead of chasing symptoms, you can focus on the 2–3 failures that actually move the needle.
Before starting Atla, I led product and engineering at two fast-growing startups and researched iterative self improvement of large language models at the Stanford Existential Risks Initiative.
At Atla, we’re a small, highly technical team of AI researchers and engineers obsessed with evaluation and reliability. We previously trained Selene, an LLM-as-a-Judge model downloaded 60k+ times.
If you’re building agents, we’d love for you to try Atla: https://www.atla-ai.com/
Know teams building agents? Please share this post with them!
Thank you!