Evals

You can evaluate AI agent workflows deterministically by applying structured scoring, AI-based rubrics, and human reviews to capture transcripts and detect regressions. This framework ensures auditable, repeatable experiment results across models.

Comparing LLM models and prompts requires a deterministic framework that manages test cases, golden outputs, and transcripts. This enables cross-model comparisons and regression detection with science-grade, auditable evaluation results.

Yes, you can run automated prompt evaluations using a CLI-first orchestration pattern. By executing a CLI script with a prepared use-case, you initiate the evaluation process against managed test cases and golden outputs.

Yes, the evaluation framework supports deterministic, AI-based, and human graders to measure capability and quality. It combines these scoring methods to deliver comprehensive, science-grade evaluation results across diverse tasks.

To detect regressions in AI agent performance, run repeatable evaluations that capture transcripts and apply structured scoring. Cross-model comparisons and deterministic test cases help identify when output quality degrades.

You do not strictly need a database, as the framework uses a file-based store to manage prompts, test cases, and results. An optional SQLite integration is available if you require faster queries across large evaluation datasets.