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Usage counts can’t tell helpful from repaired. Follow tasks to a usable result, count the human work in the exceptions and compare against a fair baseline.

Demos use clean documents. Check the evidence before the answer, plan for the moment the workflow cannot continue and leave a record support can use.

Sixty percent of what? Long-running AI work should show what is done and checked, ask for help in a way people can answer and resume from the current facts.

A shared SDK makes a switch look small. Define your own result format, note what the provider remembers, test real cases and switch where you can watch.

An agent saying it moved the delivery is not proof. Write cases with clear answers, inspect what actually changed and widen the cases before trusting a score.

Long-running AI work needs a record outside the chat. Use fixed workflows where you can, check what is true before resuming and define done in advance.

AI can speed up building a portal, but someone must own it: who can do what, how a wrong result gets explained and what arrives besides the application.

When an AI mixes up a draft and an approved plan, a better prompt may not help. Give documents roles, retrieve the right evidence and keep working state apart.

Using AI and checking its output go together. Review the requirement as well as the code, and keep domain checks part of normal engineering.

Reading messages, looking up accounts and sending replies are safe alone and risky together. Separate reading, drafting and sending before connecting AI.

A demo booking proves the idea, not the system. Validation, permissions, cancellations and ownership turn a working prototype into something you can run.

AI can turn messy inquiries into CRM fields, but missing and ambiguous answers need rules. Define tidy, validate extraction and show staff what the model found.