AI makes it cheap to produce more stones. It does not decide which cathedral is being built.
That distinction matters as generation becomes abundant. Teams can produce code, tests, documentation, configurations, analyses, and architectural alternatives faster than before. Each local piece may look impressively complete. The whole can still become harder to understand. Eventually someone asks how a component works, and the answer is a sequence of tools that generated descriptions of one another. The quarry has excellent documentation.
When production becomes abundant, coherence becomes scarce. Coherence means the parts continue to serve an intelligible purpose together. A team can trace a component to an outcome, understand its interfaces, identify its owner, inspect the evidence that justified using it, and change it without losing the account of the system. That work becomes more consequential as production accelerates.
Large engineering disciplines already know something about this. NASA treats requirements, interfaces, configuration, technical data, and assessment as managed processes across a project. Most organizations do not need spacecraft-level ceremony for everyday agent work. They do need a proportionate way to explain how the parts fit.
For agent work, start with a bounded work packet. State the purpose, the component being changed, the constraints, interfaces, permitted tools and actions, and acceptance criteria. Identify the person accountable for the outcome. Give the agent room to solve the problem inside that boundary. Then require a usable record of what changed and why. Local autonomy is easier to grant when the surrounding geometry is clear.
Interfaces deserve special attention. A component can be correct under its own assumptions and fail when connected to another component operating under different ones. A field means “ready” to one system and “approved” to another. A tool exposes an action the agent’s task did not authorize. A policy change reaches one part of the workflow while another continues using an older interpretation. Evaluation should follow those crossings and examine what meaning, authority, and evidence survive the handoff.
Acceptance has to be a real act. The agent finishing assigned steps does not by itself establish that the result belongs in the operating system. Someone or some independently governed process needs to check relevant claims against agreed criteria. The accepting function can use AI, but it needs a basis for judgment beyond the maker’s account of its own success. Generating the work and its certificate of excellence in the same breath is efficient in a way that should give us pause.
Reusable patterns raise the stakes. A sound template can help many teams. A flawed template can distribute the same defect with equal enthusiasm. Review effort should scale with replication radius and impact if wrong. The first ten minutes spent understanding a widely reused agent instruction may matter more than the next thousand outputs it produces. Production volume is a poor substitute for examining the thing being multiplied.
The record left behind is part of the deliverable. Future maintainers need to know which decisions were made, which alternatives were rejected, what constraints governed the result, and how to repair or retire it. The institution should be able to continue after the original architect changes jobs, the model changes versions, or the preferred tool becomes somebody else’s acquisition announcement.
This is where governance becomes constructive. Shared structure lets many people and agents work with real freedom while preserving the whole. More autonomy becomes possible because work has parentage, interfaces, acceptance, and memory. The architecture creates room for speed the institution can actually use.
The quarry is marvelous. Use it. But at the end of the project, people should be able to walk into the thing they made.
A cathedral is a relationship among stones. The quarry cannot ship that for us.