Observability and Application Monitoring · Founded by Maurice Burger & Roman Engeler in 2023

Atla AI

Evaluation and observability layer for AI agents using judge‑grade LLMs.

Cost

Free Trial, Paid

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Atla AI to monitor, debug, and improve AI agents by tracing every step of interactions, identifying recurring failures, and running prompt/model experiments. It surfaces root‑cause error patterns and gives actionable suggestions based on analysis of agent runs. Ideal for teams building complex agentic systems in production—helping you ship reliable AI agents more confidently.

What Atla AI does

Trace every agent interaction step (tool calls, thoughts, outcomes)Automatically detect recurring failures across runsSurface root‑cause error patterns with suggestionsCompare prompt and model performance side‑by‑sideRun experiments to test changes in behaviorIntegrate into CI/CD for production agent evaluationBuilt-in LLM judge models (Selene, Selene Mini) for evaluationRoot-cause detection from aggregated trace patternsPrompt/model comparison tools for A/B testingSeamless integration with popular agent frameworksOpen-source evaluation models available on Hugging FaceDashboard for monitoring agent reliability and failures