The sixth installment answers the question that decides the economic viability of agents: why should an agentic cycle cost less on every run? Williams's answer is that it doesn't, spontaneously. Without a deliberate mechanism for capturing and institutionalizing lessons, agents offer no cumulative advantage — they restart from zero knowledge every cycle. The component that changes this is the P7 phase, Distill.
Distill has two halves. The first is simplification. Counterintuitively, architectural review and deduplication should happen after the merge, not before. Deduplicating before the code exists is speculative; deduplicating after targets patterns that are actually observable. Tests, already in place, guarantee behavior preservation during this cleanup, which lets less capable — and therefore cheaper — models participate without risk.
The second half is lesson mining, organized as a "lesson foundry." This foundry turns recurring findings into permanent defenses: deterministic problems become lint rules paired with tests; contextual patterns feed a skill-mining pipeline; specification gaps trigger new questions in the interrogation phase. The underlying economics are decisive: each lesson is paid for once, then demoted from expensive probabilistic detection to free deterministic prevention.
Williams identifies two enemies of compounded gain. First, skill rot: stale artifacts deliver misinformation with authority; the countermeasure is a weekly verification pass that extracts checkable claims (commands, paths, versions) and flags their freshness. Second, the model ratchet: after each release, re-audit existing code with frontier models to catch what earlier versions had missed — a ratchet that allows no backward slip.
The installment culminates in the right unit of account. Rather than tracking tokens per developer, successful programs measure cost per merged, verified change. This reframing transforms how spend is read: prosecution-phase costs are not waste but investment. Four indicators reveal a broken loop: spend concentrated in the Build phase signals missing skills; increasingly expensive prosecution signals lessons that were never repatriated; repeated hits against the iteration cap signal weak specs; and a flat cost trajectory signals system failure. The thesis holds in four words: "flat cost is failure." A healthy system sees its cost per change measurably decline as skills accumulate and lint layers thicken.