Writing
2026
How to check whether an AI agent actually did the job
Test the tool action and the resulting record, file or job before treating an AI agent’s reply as proof of completion. A Patriot software investigation shows why test duration and recorded actions matter.
How to test an AI evaluator before trusting its scores
Check whether an AI evaluator catches the failures that matter, rather than just agreeing with labels. Worked examples show how to test false passes, bias, uncertainty and the cost of a wrong verdict.
How to correct an AI agent’s memory when facts change
Find the summaries, plans and drafts that used an outdated fact, repair them and test the next session. Historical maps show how a corrected source can leave copied errors in circulation.
Who is responsible when AI agents hand off work?
Give every agent handoff a named owner, the current task status and a clear next action. A safety investigation shows how separate work records can hide a critical dependency.
What makes enterprise AI worth the cost?
Calculate whether AI saves useful work after adoption, human review and operating costs. A historical factory example and an interactive model show why a cheaper answer can still mean a more expensive process.
How human review bottlenecks limit AI automation
Calculate whether reviewers can finish the work an AI system sends them. Staffing, repeated checks and rework can create a growing backlog even when automation reduces total labor.