Score Freely with Pydantic Logfire

Annotations provide a robust mechanism for evaluating automated agent runs, allowing support leads to impart feedback directly into the system instead of relying on ephemeral communication like Slack messages. This system enhances the development process by enabling reviewers to record structured verdicts—pass, neutral, or fail—along with categories for failure modes and expected outputs, turning incorrect runs into valuable training examples. The annotation process is designed for efficiency, enabling rapid grading of multiple runs and accommodating multiple reviewers to capture differing human judgments. Annotations persist beyond the lifespan of the runs they evaluate, allowing for continuous improvement of evaluation metrics by highlighting discrepancies between human and automated judgments. By exporting these insights into usable formats like JSONL or CSV, businesses can refine their evaluation processes, ensuring that automated systems align more closely with domain-specific knowledge and business needs.

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