Agents don't run. They behave. Same input produces a distribution of outputs, not an answer. Once behavior replaces output, the whole premise of testing shifts.

We require decision traces, calibrated uncertainty, and interpretability from every AI agent before we'll trust it. For ourselves, we call these "nice to have when the calendar clears."

Mayer's Law of Semantic Decay: Adoption and meaning always move in opposite directions.

A2A standardizes agent calls. It doesn't fix scattered state. Building a blackboard architecture did. Mostly.

Teachable Machine works because transfer learning constrains the problem. The same constraints that make fast demos possible also define where they fail.

Modern agent systems rediscovered coordination, but forgot the shared board that makes uncertainty explicit, auditable, and adaptable under real-world ambiguity.

Ten pressure-test questions that expose what polished AI demos miss, from hidden assumptions to user-facing failures, before production finds them.

AI demos can look brilliant while hiding brittle systems. This is about seeing through the applause and finding what breaks first.

AI agents plan like grandmasters in changing environments. The fix is simple and hard: teach them when to reassess, stop, and adapt.

AI made generation abundant. The new bottleneck is comprehension: can anyone read, challenge, and prune machine-written code before reality does?