SFEIR article (in French) that formalizes an **AI-driven SDLC in 11 phases (0 to 10)** and argues that the industry is converging on it. Starting observation: in 2025, organizations added AI tools without transforming their operating model — hence a paradox of "everything changes… and nothing changes" (execution speed multiplies without a proportional gain). The real answer is not a choice of tools but a **redesign of the cycle** for machine-led execution. The SFEIR cycle rests on **three immovable human gates** (Define, Plan, Ship), automatic phases between them, and **two compounding moments** (Compound-1 pre-deployment, Compound-2 in production) that turn lessons into reusable rules. Three principles: **AI executes** (complete artifacts + proof of execution, never trusting the agent's claims), **the human retains control of intent**, and **the system learns cumulatively**. Measured results (a redesign from 6 months to 1 day, **−30% of iterations** after ten cycles) and claimed convergence with ADLC, Google, and DORA 2025.
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.
Second installment of Chris Williams's ADLC series: it unrolls the cycle that follows from the "first law" — eight phases (P0 Triage → P7 Distill), a deterministic gate between each pair, and exactly two mandatory human moments (spec approval at P1, behavioral acceptance at P6). Key principle: an LLM→LLM handoff without a deterministic checkpoint multiplies error rates; and a "barbell" cost distribution (heavy at both ends, light in the middle) that inverts agile economics.
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.
Fourth installment in the ADLC series: Williams reframes code review as adversarial "prosecution" rather than collaborative evaluation. Charter agents to refute ("find what's wrong"), deploy single-lens reviewers with fresh contexts (correctness, security, contract compliance, spec alignment, test quality), act only on verified findings (reproduced by a failing test), and loop until two consecutive passes yield zero findings. Measure calibration by planting known bugs, mutation-testing style. Exit gate: zero open findings, two dry passes, green tests, empty test diff.
Fifth installment of the ADLC series: orchestrating parallel agents without "merge hell". Williams sets out three coupled dials — cost (model selection), wall-clock time (parallelization width), and accuracy (contract quality) — and an architectural principle: "control flow is code; judgment is models" (deterministic scripts orchestrate, models supply only judgment). Four lanes (frontier Contract Desk, single-writer Builder Pool, shared Prosecution Pool, sequential Integrator), a merge-conflict forecast built from four signals (certified width typically 3-5 agents), and consensus-based disambiguation across N cheap agents rather than clarification questions.
Sixth installment on the ADLC: Williams describes the P7 "Distill" phase as the component that drives cost down on every run. Two halves: post-merge simplification (deduce after the code exists, not before — "deduplicating before the code exists is speculative") and lesson mining (a "lesson foundry" turns recurring findings into lint rules, skills, and new interrogation questions). Each lesson is paid for once, then demoted from expensive probabilistic detection to free deterministic prevention. The right unit of account is "cost per merged, verified change," and "flat cost is failure."
Seventh and final installment of the ADLC series: Williams presents an open-source toolkit of eighteen tools built *with* the cycle itself (build-prosecute-fix loop, parallel agents, a frozen `@adlc/core` core followed by fan-out — "pinned means merged"). The doctrinal core is "frontier-free": hitting accuracy targets with mid-tier models (Opus/Sonnet/Haiku-class) rather than frontier ones, via five substitutions (search replaces insight, decomposition replaces horizon, banking replaces presence, measurement replaces metacognition, the generator-verifier gap keeps the engine running), with the human remaining the "frontier" tier at the two spec gates. Throughline of the series: "replace trust with structure, and structure with measurement."