SFEIR article (in French) that formalizes an **AI-driven SDLC in 11 phases (0 to 10)** and argues that the industry is converging toward it. Starting observation: in 2025, organizations added AI tools without transforming their operating model — producing a paradox of « everything changes… and nothing changes » (execution speed multiplies without proportional gain). The real answer is not the choice of tools but the **redesign of the cycle** for machine execution. The SFEIR cycle rests on **three immovable human gates** (Define, Plan, Ship), automatic phases between them, and **two capitalization 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 own claims), the **human retains control of intent**, the **system learns cumulatively**. Measured results (redesign 6 months→1 day, **−30% of iterations** after ten cycles) and claimed convergence with ADLC, Google, and DORA 2025.
Engineering write-up from Anthropic's **Data Science & Data Engineering** team (Chen Chang, Clement Peng, Justin Leder, Johanne Jiao, Josh Cherry) published on **June 3, 2026** on the Anthropic blog (*Enterprise AI* category, focus on **Claude Code**). **Headline result**: ***"95% of business analytics queries are automated by Claude, with ~95% accuracy in aggregate"*** (up to **~99%** in certain domains). **Core problem**: analytics is **not** code — *"there's often only a single correct answer using a single correct source"* — it requires **mapping a user question to precise, up-to-date entities** in the data model. Three **failure modes**: (1) **concept↔entity ambiguity** (e.g. *"active users"*: which actions? exclude fraudsters? which window?); (2) **staleness** (assets and the agent's knowledge become *"subtly wrong"*); (3) **retrieval failure** (*"80% of failed queries had the information present in the corpus"* but unfindable). **Solution = a 4-layer "agentic analytics stack"**: (L1) **Data foundations** — dimensional modeling, **canonical datasets** *"single source-of-truth"*, metadata *"as a first-class product"*, integrity via CI/CD; (L2) **Sources of truth** in decreasing order of trust — **semantic layer** (the agent is *"structurally required (by skill instruction) to leverage the semantic layer first"*), lineage graph, **query corpus** (distilled into structured docs, **not** raw retrieval), business context (knowledge graph: roadmaps, decision logs, org); (L3) **Skills** — the decisive lever: ***"without skills … didn't exceed 21% … Adding skills gets these numbers consistently above 95%"***; structured **in pairs** (*Knowledge skill* = router to ~30 reference files; *Unbook skill* = senior analyst workflow: clarify → find sources → execute → **adversarial review**); **colocated** maintenance (*"a code-review hook flags any reporting-model change that doesn't touch a skill file"* → **~90% of data PRs include a skill change**); (L4) **Validation** — offline evals (threshold ~90% to launch an agent, target ~100%), **ablation testing** (notable negative result: raw grep across thousands of SQL files → accuracy moves *"less than a point"*), online (adversarial review: **+6% accuracy, +32% tokens, +72% latency**), **provenance footers** (source tier + freshness + ownership), **active correction harvesting** (scheduled agents scanning channels to draft markdown fixes). **Strategic insight**: *"documentation generated, definitions owned by humans"* — letting the LLM **define** metrics was *"net-negative"*. **Minimal starting point**: a handful of canonical datasets + a few dozen evals + a *thin knowledge skill* capture *"most of the upside"*. Strongly converges with [[shihipar-claude-code-lessons-building-skills-2026-06-03]] (skills = folders, Gotchas, hooks), the *systems around the model* doctrine of [[dropbox-okumura-beyond-code-generation-engineering-productivity-ai-agents-2026-05-28]], the **semantic layer / ontology** of talisman-modern-data-101-ontology-pipeline-refresh-2026-05-04 and seale-semantic-agent-model-harness-ontology-data-2026-04-17, the *context development lifecycle* of debois-tessl-context-development-lifecycle-ai-coding-agents-2026-02-19, and the UDA/knowledge graph of netflix-uda-unified-data-architecture-knowledge-graph-2025-06-12.
#self-service analytics#agentic data analytics#Claude Code
**Chen Chang · Clement Peng · Justin Leder · Johanne Jiao · Josh Cherry** — équipe **Data Science & Data Engineering d'Anthropic**. Article publié le **3 juin 2026** sur le blog Anthropic (claude.com/blog) · catégorie *Enterprise AI* · ~5 min de lecture.