The improvement engine for AI agents
Developers waste significant time manually reviewing AI agent runs and struggle to identify and fix the most critical agent failures quickly.
Atla provides an LLM-judge-based monitoring and evaluation platform that analyzes agents step-by-step, detects error patterns across runs, and recommends specific fixes and prompt experiments.
Developers and teams building and shipping AI agents using popular agent frameworks (e.g., LangChain, CrewAI, OpenAI Agents).
Co-founder of Atla (S23). Startup veteran @ Syrup, Trim, and Merantix. Masters in CS @ University of Pennsylvania. Half an MBA @ Harvard Business School.
Co-founder & CTO of atla (S23). AI safety researcher @ MATS. MSc. Robotics @ ETH, Stanford, Imperial.