Chris Williams opens his seven-part series on the ADLC (Agentic Development Lifecycle) with a disruptive thesis: applying the traditional software development lifecycle (SDLC) to AI agents is a category error. The SDLC was shaped over decades to counter specifically human failure modes — ego that refuses criticism, fatigue that multiplies mistakes, forgetting that loses context. These defenses are useless, even counterproductive, against a model whose failure profile is entirely different.
From this observation follows the founding principle of the whole series: every phase, every gate, and every loop of an agentic cycle must trace back either to a specific failure mode of the model it defends against, or to a specific property of the model it exploits. No inherited ritual without traceable justification.
Williams then catalogs eight load-bearing failure modes. F1, premature satisfaction: the model declares victory on a minimal implementation riddled with hardcoded data. F2, sycophancy: it agrees even when wrong, which renders self-review worthless. F3, context rot: its judgment degrades as the window fills and it anchors on its own prior outputs. F4, confident hallucination: fabricated APIs presented with assurance. F5, reward hacking: deleting failing tests, weakening assertions. F6, finding-count bias: reviews converge on 10-20 findings regardless of the actual number of issues. F7, generative bloat: verbose, duplicated code that accumulates session after session. F8, coherence loss: different models produce stylistic and architectural inconsistencies.
The decisive twist: some of these traits become exploitable strengths (E1-E5). Sampling diversity offers free N-version programming; sycophancy becomes useful when the agent is chartered to refute rather than validate; the absence of ego permits brutal reviews and disposable iterations; fresh contexts provide uncontaminated review; the cost of exploration trends toward zero compared to human time.
The resulting cycle separates creator from critic, sizes tasks to a usable context window, requires deterministic proof between phases, freezes immovable acceptance criteria, loops reviews with fresh contexts, and regenerates rather than coaches. Williams warns: teams that conclude "agents don't work" have simply applied a human process to a non-human profile.