Using Evaluation Frameworks with Agent Observability

Datadog Agent Observability provides a solution for operationalizing evaluation frameworks like DeepEval and Pydantic Evals, which are often challenging for AI teams to scale beyond local experimentation. While open-source libraries offer flexibility, they require custom integration code that can be cumbersome and non-scalable. In contrast, SaaS platforms may sacrifice flexibility for convenience. Datadog addresses these issues by allowing teams to run their existing evaluations natively, maintaining the integrity of open-source frameworks while offering infrastructure for continuous evaluation runs and trace-linked regression visibility. This integration ensures that evaluations remain connected to production traces, allowing for real-time monitoring and quality assurance across development and deployment stages. By linking eval scores to production data, teams can quickly identify and address regressions as they occur, enhancing the reliability and effectiveness of their applications.