Augment Code guide (Paula Hingel) describing how AI agents are restructuring the software development lifecycle (SDLC), stage by stage. Thesis: AI produces **higher throughput in some stages and higher instability risk in others** — a symptom of uneven adoption without redrawing review boundaries. Draws on **DORA 2025**: AI adoption correlates positively with delivery throughput and product performance, but **negatively with stability**. Six stages revisited (Requirements, Design/Architecture, Implementation, Testing/QA, Deployment, Maintenance), three major risks (erosion of the junior pipeline, **circular validation** of AI-generated tests, governance gaps at scale), and three emerging roles (**Intent Engineering**, Agentic DevOps, AI Governance/Assurance). Actionable recommendations: audit one stage before scaling, stress-test governance, make **specification** central, define explicit rollback policies, redesign the junior role around review.
Op-ed by **Olivier Rafal** (Consulting Director Strategy, **WeNvision** — **SFEIR** group; former editor-in-chief of *Le Monde Informatique*) published on **June 1, 2026** in **CIO-Online**, structured around a **paradox**: in the AI era, software engineering **changes everything… and nothing changes**. **What changes = the operating model.** Roles are redefined: the **Product Owner** shifts from backlog breakdown to **generating context usable by AI**; the **developer** shifts from writing code to **framing, directing, and reviewing** agent execution; **QA** gains the ability to define **expected proof** upfront. Team structure shifts from *"double pizza teams"* (hand-off chains of ~8 people) to ***"sandwich teams"***: a **tight pairing of a business expert and a tech lead, both AI-augmented**, with other skills in support. Internal **Sfeir** figure: *"this pairing now drives roughly 80% of the production chain"*, with the remaining ~20% (architecture, data governance, security) centralized. Pivot quote: ***"The issue is not a tooling issue, but an operating-model issue."*** **What doesn't change = the discipline of the cycle.** The **SDLC** phases (define → build → verify → deploy → maintain) remain identical and non-negotiable; AI removes none of them, it **intensifies** them: ***"all the slack that human-paced work absorbed, one way or another, becomes, at AI speed, industrial-grade defects"*** (amateur-vs-professional sport metaphor). Hence **three inviolable *gates*** (human control): **specification, planning, delivery review**; validation **by proof** (not by AI's own assertions); **systematic capitalization** (each cycle enriches the next) → measured result: **−30% correction iterations after ~10 cycles**. Principle: ***"the faster the execution, the stricter the framework must be."*** Concepts drawn on: **harness** (agentic rules adapted to context), **vibe-coding** deemed **untenable in the enterprise**. **Third pillar = governance, FinOps & value-driven steering**: **variable and recurring** AI costs (~**€10/hour** per augmented seat), a shift from flat-rate licensing to usage-based billing (a 2010s cloud parallel); **FinOps** does not aim to cut costs but to *"optimize tool efficiency"* (cost weighed against value); aligning **business metrics** upfront (time-to-market, features, performance, eco-design). **Conclusion**: acceleration makes the fundamentals **non-negotiable**; the challenge is **organizational and cultural**, not technological — without securing the business relationship and collective discipline, an AI-boosted SDLC merely **amplifies problems** (hitting the wall faster). Extends the WeNvision doctrine from [[rafal-wenvision-ia-generative-produit-techno-pas-projet-2024-02-23]] and [[rafal-wenvision-tokenomics-foundation-finops-ia-2026-06-04]]; converges with *systems around the model* [[dropbox-okumura-beyond-code-generation-engineering-productivity-ai-agents-2026-05-28]], *harness engineering* [[osmani-agent-harness-engineering-2026-04-19]], agentic Salesforce, and the *agent manager* debate (BFM/Girard, SFEIR).
**Olivier Rafal** · *Consulting Director Strategy* chez **WeNvision** (groupe **SFEIR**). Ancien **rédacteur en chef du *Monde Informatique*** · et auparavant consultant analyste du marché IT (~10 ans). Tribune publiée dans la rubrique *Tribune* de **CIO-Online**. Publié le **1er juin 2026**.
Op-ed by **Olivier Rafal** (Consulting Director Strategy at **WeNvision**) published on **February 23, 2024** on **CIO-Online** (*Tribune* section), which puts forward a thesis that was still counterintuitive at the time: **generative AI is more a matter of technological product than of an AI/data science project**. **Argument 1 — data science is not the core of the issue**: building a *foundation model* from scratch requires *« several months, millions of euros, and access to enormous quantities of data »* — reserved for players with specific, monetizable datasets (e.g. **Bloomberg** and its **BloombergGPT** for finance). For nearly all companies, the right reflex is therefore not to hire data scientists. **Argument 2 — a skills mismatch**: what's mainly needed are **development and integration engineers** (back/front), **strong cloud skills**, and **DevOps**. Client quote: *« You don't necessarily need to be a data scientist, but you do need to understand the basic concepts, have back-office development skills, and strong cloud skills. »* **Argument 3 — platform architecture (orchestrators + API)**: building an enterprise **plateforme d'IA générative** via orchestrators and API makes it *« easy to work with the best LLMs on the market and switch between them as they each evolve, without reworking the applications »* (anti vendor lock-in). **Argument 4 — from project to product**: *« The platform […] must itself be considered a product »*; instead of a one-off investment, plan for a **monthly funding stream** (continuous iterations, ongoing innovation). **Argument 5 — governance & shadow AI**: the unprecedented democratization of GenAI generates *« as much shadow AI as strong expectations toward the DSI »* → governance to capture business needs, **prioritize products by value**, and oversee proper operation. **Paradigm shift** announced: *« we are moving from classic algorithmic programming to agents Langchain that handle part of the decisions »*. **Relevance for the watch**: a **founding text (2 years ahead)** of WeNvision doctrine (product > project, platform/API, flow-based funding, governance, shadow AI) that the fiches [[wenvision-ai-agents-enterprise-deployment-2025-10-01]], [[habert-ia-agentique-production-2025-10-29]] and [[rafal-wenvision-tokenomics-foundation-finops-ia-2026-06-04]] will extend (FinOps/token, flow-based funding → financial governance). It also prefigures the *harness/platform around the model* (Dropbox/Okumura: *systems around the model*) and **model independence** through an orchestration layer.
#generative AI#technological product#product vs project
**Olivier Rafal** · *Consulting Director Strategy* chez **WeNvision** (cabinet de conseil FR). Tribune publiée dans la rubrique *Tribune* de **CIO-Online**. Auteur déjà présent dans la veille (cf. fiches WeNvision/Atlas/Tokenomics). Publié le **23 février 2024**.