#Workflow reliability

Check an archive before an AI agent extracts it. Define the destination, allowed file types, overwrite behavior and resource limits before accepting files.

Bind AI publication approval to the exact version, destination and audience. A practical review record helps prevent later edits from bypassing the decision.

Check how separate AI coding changes interact after integration. Use a combined-version review record to catch behavior gaps that a clean merge can miss.

Keep spreadsheet cleanup from changing what your data means. Give an AI agent column rules, reversible transformations and an exception record to review.

Resume an AI workflow safely after access expires. Reconnect the right account, reconcile earlier writes and continue only the items whose state is known.

Distinguish an AI draft from a saved, sent or published result. Use a delivery-state reference to describe the evidence, destination and audience accurately.

Choose what to simulate in AI agent tests and what needs a real check. A reference for test data, mocked responses, side effects and honest evidence limits.

Check what an AI agent actually read from a document. Map text, table structure, footnotes and images to the evidence needed before using an extracted claim.

Distinguish an AI task deadline from a tool timeout or expired access. Use a time-limit reference to budget work and interpret what each clock controls.

Classify AI tool failures by the decision they require. A reference for fixing inputs, inspecting state, waiting safely and asking an authorized owner.