Long-form guide from **Anthropic** by **Louis Claxton** (Applied AI team), published on **August 21, 2026** on the claude.com blog: a stated **40-minute** read, roughly **64,000 characters**, presented as a collection of *plays* drawn from the team's work with its clients. (A) The diagnosis: with code no longer the bottleneck, it shifts to the stages on either side of the build (plan, review/test, deploy), line-by-line controls stop holding once the agent writes most of the diff, and governance cost rises as exceptions still route through periodic committees. (B) The response: six stages (Plan, Design, Build, Test, Deploy, Maintain) organized as a **loop** rather than a chain, each ending with a **committed artifact** that the next stage reads — `intent.md`, `spec.md`, `plan.md`, the diff and its tests, the PR and its findings, the incident record. (1) Institutional knowledge becomes versioned files: `CLAUDE.md`, skills, `REVIEW.md`, `bands.yaml`. (2) Governance splits into two layers, with the skill positioned as an advisory control and the hook as the deterministic layer behind it. Separation of duties is set as an invariant — the agent that writes the code cannot approve it — and the piece closes on *"The loop keeps running. Human judgement stays above it."* The corpus already holds [[clinton-anthropic-secure-ai-native-sdlc-2026-07-21]] on the security side of the same cycle, and [[hingel-augment-how-ai-changes-sdlc-six-stages-2026-06-08]] on the same six-stage breakdown as seen by a competitor.
#AI-native SDLC#software development lifecycle#plays
Louis Claxton (Anthropic, équipe Applied AI) · sur le blog claude.com ; contributions créditées à Jim Blackhurst · Will Steuk et Jamal Arif.
**Boris Cherny** (Creator & Head of Claude Code @Anthropic) publishes a framework table on LinkedIn, **« Steps of AI Adoption »**, mapping an engineering team's adoption of agentic AI across **5 stages (0→4)**, each characterized by an **order of magnitude of agents driven** and a **transformation of the engineer's role**: **0 Gated** (0 agents, locked-down access), **1 Assisted** (~1 agent — "you + one agent", supervised pair programming), **2 Parallel** (~10 agents — **orchestrator**), **3 Supervised autonomy** (~100 agents — **manager of managers**, an org tree), **4 AI-native** (~1,000+ agents — **VP steering by intent**). The table crosses five columns: number of agents, *what it looks like*, *the bottleneck*, *the products that help*, *the guardrails*. **Central thesis**: consuming more tokens does not move you up a level — advancing to the next stage requires **identifying and breaking the next bottleneck** AND **building the next set of guardrails**. Concretely: giving Claude a trustworthy **self-verification loop** (tests + build + lint + e2e on a real environment), enabling **Auto mode** (avoiding blocking permission prompts), making **code review and security review the default**, adopting multi-agent interfaces (Agent view CLI, Desktop, iOS/Android apps, Tag), then `/loop`, `/batch`, `/goal`, **dynamic workflows** and **worktree isolation** for subagents. On steering: usage (dashboard) measures **activity, not return**; the right question is *"would we have spent engineering effort on this anyway? if so, how many manual engineer-hours would it have cost?"* — that's the ROI. The real payoff arrives when **fixing and maintaining happens in the background** and teams focus on *building*. Anthropic sits at **stage 3, heading toward 4**; Boris Cherny states he has personally reached **level 4**.
#Boris Cherny#Claude Code#Anthropic
Boris Cherny (Creator & Head of Claude Code @Anthropic)
Case study published by the **Cornell AI Innovation Hub** (June 15, 2026): how a two-semester collaboration between the AI Hub, graduate students, and Cornell's Treasury team turned a time-consuming manual investigation into an AI tool that **recovered $100,000** in unidentified payments on a first batch. A successful **AI4Business** use case (financial process) that illustrates the **Leader-Lab-Crowd** framework of **Ethan Mollick** almost point by point: the **AI Hub** plays the role of the **Lab** (a central, ambidextrous team of technologists plus students); **Treasury** (Cheryl Barnes, Marie Graves…) is the **Crowd** carrying business knowledge and the real pain point; and the **$100,000** constitutes the **visible reward** (vivid win) that anchors adoption — exactly the incentive lever Mollick considers decisive. Key method: **"context first, then plan, then build"** via **Claude Code Plan Mode**, a chain of **fuzzy matching → Gemini Enterprise Web Search → Claude synthesis**, all within the governed **Cornell AI Gateway**. *"The $100,000 is a start."*
#Cornell AI Innovation Hub#unidentified payments#payment reconciliation
**Pete Stergion** — Desktop Engineer au Cornell AI Innovation Hub · co-tech lead du projet (avec Phil Williammee). Article institutionnel signé de l'AI Hub.
Interview with Andrej Karpathy (OpenAI co-founder, former Tesla Autopilot) moving from *vibe coding* to *agentic engineering*: December 2025 as the turning point "never felt more behind as a programmer," the Software 1.0/2.0/3.0 taxonomy, the openclaw example (bash script → text to copy-paste into the agent) and MenuGen rendered obsolete by Gemini's Nanobanana, the *verifiability* theory explaining why LLMs are *jagged* (math/code peak, "walk to the car wash 50m away" fails), the distinction between *vibe coding* (raise the floor) and *agentic engineering* (preserve the quality bar), the "animals vs ghosts" metaphor, the overhaul of hiring via agent-versus-agent projects, and the key formula: ***"You can outsource your thinking but you can't outsource your understanding."***
#Andrej Karpathy#vibe coding#agentic engineering
Andrej Karpathy (co-fondateur OpenAI, ex-Tesla Autopilot, créateur du terme "vibe coding")
Cat Wu and Boris Cherny (Anthropic) explain how to use Claude Code like its creators: antfooding, plan mode, subagents, hooks, and extensibility — Every's AI & I podcast
#Claude Code#Cat Wu#Boris Cherny
Rhea Purohit (interviewer: Dan Shipper) · Cat Wu · Boris Cherny