Engineering Adaptive AI

Your agent completed the task. What happens when the model changes, a tool fails, or the next operator takes over? Engineering Adaptive AI offers a practical framework for questioning important assumptions, retaining useful lessons, containing change and checking whether an apparent improvement survives a fresh context.

Across 15 lessons, Phillip Nakata connects ethical governance with concrete engineering practices: bounded experiments, state-aware recovery, explicit retention and authorization, useful refusals, independent review and evidence that another operator can inspect. The proposed Quanta of Learning rubric helps organize evidence of persistence and transfer for a defined task.

Written for developers, architects, technical leaders, educators and governance practitioners, the guide includes examples, counterexamples, exercises and three appendices covering teaching, incident communication and reusable records. Approximately 20,000 words; 60 pages in the current manuscript layout. AGI preparation is its longer-term ambition; the methods apply to present-day agents, and the learning rubric is a proposed evaluation convention rather than a validated intelligence score.