Get Started
Overview
Learn about Atla
Atla is designed to clarify the messiness of agent behaviour. Automatically detect errors and get insights to continually improve agent performance.
2. Span-Level Error Analysis
Rather than just logging failures, analyzes each step of the workflow execution. Identify errors across:
3. Error Remediation
Directly implement Atla’s suggested fixes with Claude Code using our Vibe Kanban integration, or pass our instructions on to your coding agent via “Copy for AI”.
4. Experimental Comparison
Run experiments and compare performance to confidently improve your agents.
Why evals?
LLMs only reach their full potential when they consistently produce safe and useful results. With a few lines of code, you can catch mistakes, monitor your AI’s performance, and understand critical failure modes to fix them. If you are building generative AI, creating high-quality evals is one of the most impactful things you can do. In the words of OpenAI’s president Greg Brockman:
Atla Workflow
1. Error Pattern Identification Find error patterns across your agent traces to understand systematically how your agent fails.
2. Span-Level Error Analysis
Rather than just logging failures, analyzes each step of the workflow execution. Identify errors across:
- User interaction errors — where the agent was interacting with a user.
- Agent interaction errors — where the agent was interacting with another agent.
- Reasoning errors — where the agent was thinking internally to itself.
- **Tool call errors **— where the agent was calling a tool.
3. Error Remediation
Directly implement Atla’s suggested fixes with Claude Code using our Vibe Kanban integration, or pass our instructions on to your coding agent via “Copy for AI”.
4. Experimental Comparison
Run experiments and compare performance to confidently improve your agents.
Next steps
- Get your API key.
- You can get started with running your first eval here.