Autonomous SDLC
A software delivery lifecycle redesigned so agents can plan, code, test, and deploy end-to-end at machine speed, with governance, guardrails, and audit evidence embedded directly in the pipeline rather than enforced via periodic manual gates.
Also known as: autonomous SDLC, machine-speed SDLC, governed autonomous pipeline
How authors use it
Oldest first (extraction date when no publication date is known).
| Author | Their term | Their meaning |
|---|---|---|
| Presentation: Platform Engineering in the Age of AI Stéphane Di Cesare, Davide de Paolis, Stephen Cihak, Camila Macedo, Renato Losio · 2026-09-08 | Autonomous SDLC | Cihak's term for a software delivery lifecycle redesigned so agents plan, code, test, and deploy end-to-end, with systematic permissions and governance embedded in the pipeline that automatically produces auditable evidence — replacing periodic manual approval gates like a weekly Change Advisory Board. |
Documents (1)
- Presentation: Platform Engineering in the Age of AI Stéphane Di Cesare, Davide de Paolis, Stephen Cihak, Camila Macedo, Renato Losio · 2026-09-08 source ↗ Reader 🖥️
AI does not change what platform engineering fundamentally is — enabling safe, fast self-service through clear service intent — but it raises the stakes and widens the scope: platform teams must now serve agents as first-class consumers alongside humans, absorb new responsibilities (distributing AI context/skills, governing AI tool licensing and MCP access, guarding against non-deterministic behavior), and answer the standardization-versus-autonomy tension with embedded, evidence-producing governance rather than manual gates — making the platform, not less relevant as AI automates toil, but more central to letting an organization move safely at machine speed.
Propositions touching this concept (2)
By publication month, oldest first (extraction month when no publication date is known).
2026-09
- explicitThe panel foresees an 'autonomous SDLC' in which agents plan, code, test, and deploy changes with systematic permissions and governance embedded in the pipeline, because periodic manual approval processes (e.g., a Change Advisory Board meeting once a week) cannot keep pace with AI-driven change volume.
- implicitThe panel assumes that regulatory and audit obligations are a fixed constraint that will not loosen as AI-driven delivery velocity rises; every proposed answer (autonomous SDLC, embedded evidence production, graduated governance) works around this constraint rather than questioning whether the obligations themselves should change.