Improve AI agent quality with Bits Evals

Rashel Hoover and Michael Bevilacqua-Linn discuss the complexities of AI agent development, highlighting the limitations of coding agents like Claude Code and Codex in handling tasks beyond coding, such as error analysis and dataset creation, which require human judgment. Bits Evals, a set of features in Preview, aims to automate repetitive tasks in the AI agent development loop while keeping engineers in control of critical decisions, allowing teams to resolve production failures and implement improvements more rapidly. By integrating user feedback directly into traces, Bits Evals enhances error analysis by providing a more accurate reflection of user experiences beyond operational metrics. It offers structured root cause analysis and generates evaluators based on real production behavior, streamlining workflows and reducing manual setup for evaluations. The system connects various stages of the development process into a cohesive workflow, utilizing Datadog Agent Observability to monitor and refine AI agents continuously. This approach allows teams to focus on high-priority issues, aligning their efforts with actual user outcomes and facilitating more informed deployment decisions.