Build a testing laboratory for AI workflows that helps developers determine whether a prompt or multi-step AI process still behaves reliably after a change.
Let users define a workflow containing prompts, input variables, expected output properties, validation rules, and optional human review checkpoints. Allow test cases to be imported from JSON or created through a visual editor.
Include:
A versioned registry of prompts and workflow configurations.
A reusable test-case library with edge cases and adversarial inputs.
Structured checks for required fields, formatting, consistency, and factual support.
Side-by-side comparisons of outputs from different versions.
Regression reports showing which test cases passed, failed, or changed materially.
Configurable evaluation criteria with explicit weights.
Cost and latency fields when real measurements are supplied.
A release gate that flags regressions before a workflow is promoted.
Support a mock execution mode so users can explore the application without API credentials. Never fabricate model evaluations, latency, or token costs. Keep test results reproducible by recording input data, configuration versions, and evaluation rules. Design the system so real model providers can be connected later through clearly separated adapters.
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