Atla transforms AI agent development by automatically identifying and fixing recurring failures that slow down shipping and erode system trust. This evaluation and improvement platform helps development teams move beyond basic observability to actionable insights that resolve the root causes of agent performance issues.

Instead of manually digging through thousands of execution traces without a clear signal, Atla's intelligent analysis automatically identifies failure patterns behind agent breakdowns and suggests targeted fixes. The platform uses advanced LLM-as-a-judge evaluation to assess agent performance step-by-step, uncovering error patterns across multiple runs while providing specific recommendations for improvement.

AI agents are complex systems that chain together plans, tool calls, and agent-to-agent interactions, making them difficult to debug when failures occur. Atla addresses this challenge by turning noisy trace data into actionable insights, allowing development teams to focus on the 2-3 critical failures that actually impact system performance rather than chasing symptoms.

The platform supports popular agent frameworks, including LangChain, CrewAI, and OpenAI Agents, providing seamless integration with existing development workflows. With real-time monitoring capabilities and automated error detection, Atla gives teams the visibility and control needed to confidently ship agentic systems that work reliably in production environments.

Built by a team of AI researchers and engineers with expertise in evaluation and reliability, Atla leverages proven techniques from iterative self-improvement research to help teams ship agent improvements in hours rather than days.

Key Features

• Automated Pattern Detection: Automatically identifies recurring failure patterns across thousands of agent execution traces without manual analysis or endless debugging sessions
• LLM-as-a-Judge Evaluation: Uses an advanced language model evaluation to assess agent performance step-by-step and provide a detailed analysis of decision-making processes
• Targeted Fix Suggestions: Provides specific, actionable recommendations for resolving identified issues rather than generic debugging advice or surface-level insights
• Framework Integration: Seamlessly works with popular agent development frameworks, including LangChain, CrewAI, and OpenAI Agents for easy implementation
• Real-time Monitoring: Continuously monitors agent performance in production with automated alerts and detailed analytics for proactive issue identification

Who This Is For

• AI Development Teams: Software engineers and AI developers building production agent systems who need reliable debugging tools and performance optimization insights for complex multi-step workflows.
• Startup Engineering Teams: Early-stage companies developing AI-powered products that want to ship reliable agent features quickly without extensive manual testing and trace analysis.
• Enterprise AI Teams: Large organizations implementing AI agents at scale who require robust evaluation frameworks and systematic approaches to maintaining agent reliability and performance.

Common Questions

How does Atla differ from basic observability tools? Atla goes beyond simple trace collection by automatically identifying failure patterns and providing targeted fix suggestions, while observability tools typically only surface raw execution data without analysis.

What agent frameworks does Atla support? Atla integrates with the most popular agent frameworks, including LangChain, CrewAI, and OpenAI Agents, with additional framework support being added regularly.

How quickly can teams see improvements after using Atla? Teams typically ship agent improvements in hours rather than days, with case studies showing development teams moving twice as fast when using Atla alongside existing debugging tools.

Does Atla require API keys or a complex setup? The free tier requires no API key and includes up to 1,000 traces with 30-day log retention, making it easy to get started without infrastructure changes or complex configurations.