I have worked with AI at Microsoft and Meta, on systems that serve hundreds of millions of people. I have beta-tested models before they were public and spent a meaningful part of my working life diagnosing what happens when AI meets real customers at scale.
None of that prepared me for the specific texture of building and maintaining AI skills in production. A few things could have been told to me earlier.
Seven observations
1. The output always looks more confident than the reasoning deserves. Fluency is not accuracy. Read the reasoning, not the prose.
2. The failures that matter most look like successes. A crash is visible. A coherent, confident wrong answer that survives for three weeks is not.
3. Duplicated information is a time bomb. Two sources eventually disagree. In an AI system, that disagreement becomes confident confusion.
4. Last month's skill is already slightly wrong. Models and processes change. Build a review cadence before you need one.
5. Absence is the hardest thing to notice. Ask what should be in the log that is not.
6. An escalation is only as good as its business case. Show frequency, user impact, and exact evidence, not just that something sometimes goes wrong.
7. The person maintaining the skill is never the person who built it. Write accordingly.
Everything else in this series is the framework. These are the things I learned after I thought I had the framework figured out.
This is part of the Parenting Your AI series, a practitioner's guide to building AI skills that are safe, effective, and worth trusting. Written from inside enterprise AI systems by someone who has spent years diagnosing what goes wrong when AI meets real work at scale.
Read the full series at KnowledgeManagement.ie