AI Workflow Acceptance Test Builder

The problem

AI workflows often get tested by demoing the happy path. The agent answers correctly once, the automation completes once, and the system is treated as ready. The expensive failures usually appear later: missing inputs, tool timeouts, duplicate side effects, permission errors, partial success, repeated retries, broken handoffs, unsupported claims, or a workflow that says “done” when the business outcome actually failed.

What it does

• Turns AI-agent and automation specs into concrete acceptance-test matrices with observable pass and fail conditions.
• Builds happy-path, missing-input, ambiguous-input, invalid-input, tool-success, tool-failure, retry, permission, and recovery tests.
• Pressure-tests partial success, duplicate actions, idempotency, destructive-action gates, downstream compatibility, and human handoff behavior.
• Checks whether tool errors and user corrections actually change the workflow route instead of producing repeated failed actions or cosmetic “self-correction.”
• Defines forbidden outcomes, failure evidence, and explicit RETRY, REVISE, ASK, ESCALATE, STOP, or BLOCK routes.
• Converts known production failures into durable regression cases and produces a release-readiness gate for UAT, pilot, or deployment.

Why this beats prompting it yourself

Generic QA prompts usually return a long list of test ideas. This skill builds a decision-bearing acceptance system. Each important test defines preconditions, inputs, expected state transitions, expected outputs, permitted side effects, forbidden outcomes, pass criteria, failure evidence, and the route the workflow must take when the test fails.

Use cases

• Build client-ready UAT packs for AI automation projects.
• Test RAG assistants for unsupported answers, missing sources, retrieval failures, and escalation behavior.
• Audit coding agents for repository scope, test failures, destructive actions, and false success claims.
• Test workflow automations for duplicate webhooks, partial failure, retries, CRM/API errors, and recovery.
• Turn production incidents into regression tests before the next release.

Known limitations

This skill does not independently execute tests, certify security or compliance, inspect implementation details it cannot access, or guarantee that every possible production failure is covered. High-stakes workflows may still require specialist QA, security, legal, compliance, statistical, or domain review.