This SFEIR article formalizes an AI-driven software development cycle in eleven phases (0 to 10) and argues that the industry is converging toward this type of model. The starting point is a diagnosis: in 2025, organizations deployed AI tools without transforming their operating model, producing a paradox summed up by the phrase "everything changes… and nothing changes" — execution speed multiplies without a proportional gain. The real challenge, then, is not choosing the right tools, but rethinking the software lifecycle itself for machine-led execution.
The SFEIR cycle chains together: 0 Setup (stack detection, project memory initialization), 1 Define (specification — human gate), 2 Plan (architecture arbitration — human gate), 3 Build (agent-driven development), 4 Verify (automated testing and coverage), 5 Review (four parallel audits: code, security, tests, performance), 6 Compound-1 (capturing lessons before deployment), 7 Ship (production acceptance — human gate), 8 Ops (monitoring and rollback), 9 Compound-2 (lessons from runtime) and 10 Deprecation (retirement and capitalization). Three immovable human gates — Define, Plan, Ship — frame a set of otherwise automatic phases; two compounding moments (Compound-1 and Compound-2) turn lessons into reusable rules that feed subsequent cycles.
Three principles structure the approach. First, AI executes, it does not assist: agents produce complete artifacts (code, tests, documentation) across entire phases, and a proof-of-execution discipline captures actual outputs — the system never trusts the agent's claims. Second, the human retains control of intent through the three gates: the human decides what to build, the machine optimizes execution. Finally, the system learns cumulatively, each cycle enriching the next.
The results put forward support the thesis: a corporate site redesign that went from six months to one day, −30% of correction iterations after ten cycles (a bug reported twice becomes an automated rule), reviews across four parallel angles, an augmentation cost of about €10/hour, and a target of 850 fully AI-augmented consultants by the end of 2026.
The article claims an industry-wide convergence with the ADLC (two gates, "intent verified exactly twice"), Google's whitepaper on the new SDLC (41% AI code, 85% of devs on agents), and DORA 2025 (AI as an "amplifier"). It finally delineates suitable use cases (back offices, APIs, migrations, automatically verifiable outputs) and unsuitable ones (unconstrained novel design, safety-critical systems awaiting standards, ungoverned data environments), and recommends starting with a rigorous specification gate and proof of execution. First installment of a seven-part series.