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.
Analysis by Janakiram MSV (The New Stack, July 20, 2026) of the **architectural convergence** of the three hyperscalers' enterprise agent platforms: in nine months, **Amazon Bedrock AgentCore**, **Microsoft Foundry**, and **Gemini Enterprise Agent Platform** have converged on the **same six primitives** — runtime, memory, tool gateway, identity, observability, governance — under different brand names. What was a fragmented collection of libraries 18 months ago is becoming a distinct **platform layer**. The thesis: this convergence replays the **2011-2016 PaaS inflection**, where **Cloud Foundry** and **Heroku** unified VMs, load balancers, queues, and secret stores around a portable **application contract** — except that here **no equivalent contract yet exists**, and **no open source project has claimed it**. Consequence: an enterprise cannot **move an agent from one cloud to another** (session state, traces, and identity all end up with a single provider; migrating means rebuilding everything). The author proposes a **line-by-line mapping** of the Cloud Foundry contract onto agents, sets out three design principles (package the agent as **one deployable unit**, **attach** capabilities rather than embedding providers, integrate the **operational** layer into the abstraction), points to what open protocols (MCP, A2A, OpenTelemetry) leave out of scope — the **lifecycle** — and delivers three due diligence questions: **governance** (neutral foundation vs. vendor), **packaging** (the same artifact on two clouds without rewriting), **state** (exportable memory). Verdict: whoever ends up owning the **agent control plane** will define *what an agent is*.
**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)