The Challenge: Staying Honest at Scale

When you’re building AI agents and skills, you face a fundamental problem:

How do you stay honest when the system is complex?

The moment your skill does something non-trivial, verification gets hard. You run the code, it produces output, but did it produce the right output? Did it make the right decisions? Or did it just look like it did because you didn’t inspect deeply enough?

There’s a name for this in engineering: the rigor problem. It’s why surgical teams have checklists. It’s why flight crews have redundant instruments. It’s why NASA counts down from T-minus 10, not 10, 9, 8…

In AI development, the rigor problem is worse because a system can fail in non-obvious ways.