One-Minute Example

A platform team asks:

Should we buy, fine-tune, or build an agent stack for our product?

Without FPF, the conversation often mixes architecture, vendor comparison, safety, evidence, budget responsibility, user value, and implementation planning. The loudest option can win before the team knows what is being compared.

With FPF, the first pass can become a small set of explicit project objects:

• holons in play: the product, the agent stack, and the team or toolchain that will change it are not the same holon;
• architecture flow: what problem pressure should become which candidate, selected, expected, and actual structures;
• comparison frame: which alternatives are in the candidate set;
• evaluation characteristics: cost, latency, controllability, safety, maintainability, time to first use, and other project-specific characteristics;
• evidence gaps: which test result makes commitment admissible;
• current decision state: whether the team is choosing now, keeping a selected set, making a project architecture decision, or doing more discovery;
• work and feedback: which method, readiness, and performed-work records establish that the selected structures were actually realized;
• reader reliance: what engineering, management, and assurance readers may responsibly rely on.

That same shape can be used for a factory modernization, laboratory protocol, construction design change, supply-chain decision, safety case, or research program. The point is not the AI topic; the point is one body of reasoning that can be reviewed, improved, and published without changing meaning on the way.