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The improvement engine for AI agents

Find and fix your agent’s most critical failures in hours, not days. Atla helps developers cut time spent on manually reviewing traces. Atla’s LLM judge evaluates your agent step-by-step, uncovers error patterns across runs, and suggests specific fixes—so you know exactly what to fix and why. Atla supports the most popular agent frameworks teams build with, including LangChain, CrewAI, and OpenAI Agents. With real-time monitoring, automated error detection, and prompt experimentation, Atla gives teams the visibility and control needed to confidently ship agentic systems that work. We’re a team of researchers, engineers, entrepreneurs and operational leaders. Our expertise in evals was honed through training our own purpose-built LLM Judges, Selene and Selene Mini, which are available open-source and have been downloaded 60,000+ times.
Active Founders
Maurice Burger
Maurice Burger
Founder
Co-founder of Atla (S23). Startup veteran @ Syrup, Trim, and Merantix. Masters in CS @ University of Pennsylvania. Half an MBA @ Harvard Business School.
Roman Engeler
Roman Engeler
Founder
Co-founder & CTO of atla (S23). AI safety researcher @ MATS. MSc. Robotics @ ETH, Stanford, Imperial.
Company Launches
Atla: The improvement engine for AI agents
See original launch post

Hey everyone! Roman here, cofounder of Atla.

TL;DR

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

❌ The Problem

Agents are complex black boxes, chaining together plans, tool calls, and agent-to-agent interactions. For this reason:

  • Failures hide inside long traces and are difficult to spot at scale.
  • Current solutions that just display traces without identifying issues are not enough.

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.

🛠 Our Solution

Atla turns all that noise into actionable insights:

  1. Error annotation: We automatically flag errors at the step level to find the root cause of a failed run.
  2. Pattern detection: We cluster those errors across traces to surface the recurring, high-impact failures.
  3. Actionable fixes: For each failure mode, Atla generates targeted recommendations specific enough to implement as small pull requests.

The result: instead of chasing symptoms, you can focus on the 2–3 failures that actually move the needle.

📈 Impact so far

  • A legal AI startup used in over 15 countries now spots prompt failures in days instead of weeks with Atla.
  • A fast-growing productivity AI startup doubled the number of improvements shipped with Atla.

👷 Why Us

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.

🙏 Our Ask

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!

Previous Launches
Half of AI’s answers are brilliant, half aren’t. We trained a model to tell them apart.
Jobs at Atla
London, England, GB
£90K - £130K GBP
0.10% - 1.00%
3+ years
Atla
Founded:2023
Batch:Summer 2023
Team Size:10
Status:
Active
Location:London, United Kingdom
Primary Partner:Harj Taggar