Running Pydantic AI agents on Jev, and seeing what they decided

Pydantic AI 2.50.0 adds support for TypeSafe’s Jev decision model, which is designed for classification tasks such as ticket routing, safety checks, and scoring rather than free-form text generation. Developers can define typed Pydantic output fields including booleans, literals, enums, bounded floats, rubrics, and unions, allowing Jev to return structured decisions with probabilities in a single request, while unsupported free-form fields can trigger errors or fall back to a language model. New DecisionModel infrastructure and decision-specific tracing record questions, selected answers, alternative probabilities, thresholds, and union-routing steps, improving visibility beyond standard chat spans. Logfire’s Live view now presents classifier outputs, confidence levels, rubric distributions, union decisions, and underlying decision spans, while respecting content-visibility settings. The integration can also be used for evaluations, through Pydantic AI Gateway, and as a foundation for additional decision-model backends, enabling teams to compare Jev with language models on their own workloads.

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