Atla
InactiveAI-nativeThe improvement engine for AI agents
What it does
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.
Next to it in Agent infrastructure · AI agent debugging and optimization
- RelaceY Combinator W23
Models and infra for coding agents
- LaminarY Combinator S24
Understand why your AI agent breaks. Iterate fast to fix it.
- SynthY Combinator F24
Prompt and Context Optimization for Coding Agents
- dari.devY Combinator F25
The Routing Layer for AI Agents
- SentrialY Combinator W26
Datadog for Agent Reliability
- ReasonBlocksY Combinator X26
The runtime layer that makes AI agents cheaper and more reliable
- GrumaticPlug and Play PnP 2026
Grumatic is an AI intelligence platform that measures prompt quality, detects cost waste, and optimizes developer productivity for AI coding agents (Claude Code, Codex CLI and so on).
- hiloopY Combinator S26
Infrastructure for recursive self-improvement