Openlayer

AI engineering and data teams use Openlayer to check whether changes to models, prompts, datasets, or agent workflows improve results without introducing quality, safety, performance, or cost regressions. It supports evaluation of large language models, AI agents, retrieval-augmented generation (RAG) applications, and traditional machine-learning systems, with ready-made and custom tests that teams can run before deployment and in CI/CD workflows. Results and versions can be compared to help identify what changed and where a system is failing. After deployment, Openlayer can trace requests and monitor AI behavior, including quality, latency, cost, and drift. Teams can also use it to examine agent task completion and tool use, test for issues such as bias or prompt injection, and keep evaluation results and governance evidence connected to the systems they assess. It may suit organizations that need repeatable AI testing and production monitoring across multiple projects.

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