Atla AI - Observability and Application Monitoring Tool
Free Trial, PaidObservability and Application Monitoring
Evaluation and observability layer for AI agents using judge‑grade LLMs.
Free Trial, PaidObservability and Application Monitoring
Evaluation and observability layer for AI agents using judge‑grade LLMs.
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.
Integrations
Use Cases
Debugging AI agents systematically in production
Evaluating prompt changes or model upgrades reliably
Detecting hallucinations or failed tool calls at scale
Improving multi-step agent workflows via insight-driven fixes
Embedding performance checks into CI/CD pipelines
Benchmarking evaluators with Judge Arena and open-source models
Standout Features
Built-in LLM judge models (Selene, Selene Mini) for evaluation
Root-cause detection from aggregated trace patterns
Prompt/model comparison tools for A/B testing
Seamless integration with popular agent frameworks
Open-source evaluation models available on Hugging Face
Dashboard for monitoring agent reliability and failures
Tasks it helps with
Trace every agent interaction step (tool calls, thoughts, outcomes)
Automatically detect recurring failures across runs
Surface root‑cause error patterns with suggestions
Compare prompt and model performance side‑by‑side
Run experiments to test changes in behavior
Integrate into CI/CD for production agent evaluation
Who is it for?
ML Engineer, AI Developer, Software Engineer, Platform Engineer
Overall Web Sentiment
People love itTime to value
Quick Setup (< 1 hour)agent evaluation, LLM judge, AI trace monitoring, agent observability, prompt experiments, error pattern analysis
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