What must reality reveal before commitment?
Field manual
What practitioners do Monday morning.
The playbook turns fast executable learning into decisions, records, checks, and a delivery baseline a serious team can trust.
The point is not to run better demos. The point is to expose uncertainty, change the working model, and preserve the evidence that should affect delivery.
Five practice modules
- Frame the uncertaintyIdentify the decision the team cannot make safely from documents alone.
- Materialize candidate realityCreate the smallest working interpretation stakeholders can operate.
- Run the Reality LoopWatch what changes when people stop imagining and start using.
- Decide and record evidenceCapture accepted behavior, rejected alternatives, open risks, and changed assumptions.
- Prepare the delivery baselineSeparate what is understood enough to engineer from what still requires checks.
Artifacts that earn their keep
Who needs to experience the candidate and why?
What did the working model make visible?
What moves forward into engineering review?
What not to do
Do not demo when you need observation.Do not sell the candidate as production.Do not skip Reality Checks because the screen looks plausible.Do not carry untested assumptions into Build to Deliver.
Carry the proof forward
See why accepted behavior, decisions, checks, and production evidence become central in AI-era delivery.