Atla AI is an evaluation and improvement layer for AI agents that automatically detects and fixes failures. It analyzes traces to identify recurring error patterns, provides actionable suggestions, and helps developers build more reliable agents by comparing performance across versions.
Freemium
How to use Atla AI?
Integrate Atla into your existing AI agent stack via simple APIs. It monitors agent interactions, clusters failures, and surfaces critical issues. Developers can then apply suggested fixes, test changes, and track improvements to enhance agent reliability without manual debugging.
Atla AI 's Core Features
Automatically monitors and evaluates AI agent traces in real-time to detect errors and anomalies as they occur.
Clusters and ranks similar failure patterns across thousands of interactions, highlighting the most impactful issues.
Generates specific, actionable improvement suggestions based on detected failure modes to guide prompt or model adjustments.
Allows side-by-side comparison of agent versions to validate changes and ensure improvements without introducing new problems.
Provides granular trace summaries with step-level annotations for deep understanding of agent decision-making processes.
Integrates seamlessly with popular tools like LangSmith and Langfuse, complementing existing observability setups.
Offers custom LLM judge metrics and scalable evaluation capabilities to suit different agent complexities and use cases.
Atla AI 's Use Cases
Customer support teams use Atla to identify and fix recurring failures in AI chatbots, reducing response errors and improving user satisfaction.
Research assistants leverage the tool to debug deep research agents, uncovering hidden failure patterns that impact data accuracy and efficiency.
Development teams building dev tools integrate Atla to automatically test agent prompts, speeding up iteration cycles and ensuring reliability.
Startups with limited resources rely on the free tier to monitor agent performance, catching critical issues early without upfront costs.
Enterprises handling sensitive data use the custom tier for self-hosted deployment, maintaining privacy while benefiting from advanced analytics.
AI product managers utilize Atla to track agent metrics over time, making data-driven decisions to enhance overall product quality.
Atla AI 's Pricing
Developer Tier
Free
Free plan with up to 2,000 traces evaluated, automatic error pattern detection, improvement suggestions, and up to 3 custom LLM judge metrics.
Startup Tier
$199/month
Includes up to 10,000 traces per month, custom onboarding, dedicated Slack support, unlimited users, up to 10 custom metrics, 60-day data retention, and SOC2/BAA compliance.
Custom Tier
Custom pricing
Tailored for high volume or privacy needs, featuring self-hosted deployment, unlimited workspaces, custom SLAs, SSO, RBAC, and engineering support.
