Chris Williams (@voodootikigod) opens his ADLC series arguing that running the human SDLC on models is a category error: the classic cycle was designed to counter human failure modes (ego, fatigue, forgetting) that are absent in LLMs. He catalogs eight load-bearing failure modes (F1-F8) and five exploitable properties (E1-E5), and lays out the founding principle: every phase of an agentic cycle must trace back to a failure mode it defends against or a property it exploits.
Third installment in the ADLC series: Williams turns testing into the specification in the only language the builder cannot contest. Where TDD is an optional quality practice for human-written code, it becomes the load-bearing trust mechanism of the entire lifecycle once agents write the code. Three "rail discipline" rules: separated authoring contexts (specs-only before implementation), mechanical freezing at the tool level (not the prompt), and adversarial audits ("does a test fail if the feature is deleted?"). Mutation testing is preferred over coverage percentage, which is Goodhart-able at machine speed.
Encyclopedic article (Wikipedia, English) on **Goodhart's law**: stated by British economist Charles Goodhart in 1975 regarding monetary policy — "any observed statistical regularity tends to collapse once pressure is placed upon it for control purposes" — then generalized by anthropologist Marilyn Strathern (1997) into the canonical aphorism "when a measure becomes a target, it ceases to be a good measure." The subject connects economics, incentive theory, public policy evaluation and, by extension, metric optimization in AI systems.
#Goodhart's law#measure becoming a target#statistical regularity
Wikipedia contributors (concept : Charles Goodhart ; généralisation : Marilyn Strathern)