Arize AI

Arize AI provides a platform designed for teams building and operating AI agents, with a primary focus on observability, evaluation, and continuous improvement. The system enables engineers and data scientists to monitor the behavior of AI agents in production, trace their decision-making processes, and assess performance against defined criteria. By capturing detailed telemetry data from agent interactions—including prompts, tool calls, external API responses, and final outputs—users can identify failures, hallucinations, latency issues, or unexpected behavior across complex multi-step workflows. The platform supports both real-time and historical analysis, allowing teams to debug problems quickly and understand how agents handle edge cases over time. A core component is the ability to run structured evaluations. Users can define custom metrics—such as correctness, safety, tone, or task completion rate—and apply them to agent traces automatically. This makes it possible to score agents against human-labeled golden datasets or against LLM-as-judge evaluations, providing a quantitative basis for improvement. The system also supports A/B testing of different agent versions, prompt modifications, or model configurations, enabling teams to deploy changes with confidence. In addition to evaluation, Arize offers experimentation workflows that simulate agent behavior across diverse inputs. Users can replay historical traces under modified conditions or test new prompt templates against a library of test cases. This helps uncover regressions before they reach production. The platform integrates with major LLM providers, vector databases, and orchestration frameworks (e.g., LangChain, LlamaIndex), so data ingestion happens automatically without manual instrumentation in many cas