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The structured, governed, memory-driven workflow for AI-enabled software development. Not "AI writes code"—AI-augmented engineering with human oversight.
The AI-enabled software development lifecycle (AI-SDLC) is the modernization of software development for an era where AI works alongside engineers—not as a magic black box, but as part of a governed, memory-driven, pattern-first workflow.
AI-SDLC is NOT "AI writes code." It is structured AI-assisted engineering.
AI outputs are inconsistent without patterns
Estimates are wildly inaccurate without grounding (10-20x too high)
Prompts leak context without a memory layer
AI-generated code introduces hidden regressions ("AI slop")
Agile ceremonies don't account for AI as a participant
Teams have no method to govern how AI interacts with code, requirements, or architecture
The old model assumes a world that no longer exists.
Your core micro-cycle. Every AI-assisted task—feature, bug fix, refactor—runs through PBVR.
Shared, persistent context. AI needs state. PBVR needs continuity. Teams need shared background.
Prevent hallucinated APIs, invented architecture, fake estimates, security oversights, and regressions.
Reusable, structured prompts. Replace ad-hoc prompting with determinism.
Connected patterns. Orchestration across planning, coding, PR review, testing, deployment.
Focused engineering time is 5-10% of naive AI estimates. MCP grounded estimates vs. AI confidence theater.
Auditable, explainable, reproducible, attributable, safe for production.
Memory loads → backlog refined → PBVR tasks created
Developers run PBVR cycles with guardrails enabled
AI-augmented testing + code scanning + regression checks
Retros → memory updates → guardrails updated → pattern tuning
This is a living lifecycle, not a document.
Engineering Managers
Tech Leads
IC Engineers
Product Managers
Founders Shipping Fast
Teams Using AI (But Drowning in Chaos)
This is the operating system for AI-enabled software development.
Traditional SDLC assumes human-only workflows with linear phases. AI-SDLC assumes continuous AI collaboration with memory, guardrails, and structured patterns.
No. AI-SDLC is the category. AI-Enabled Agile is one practice inside it. AI-SDLC covers the full lifecycle: planning, building, verifying, refactoring, governance, memory, and orchestration.
Naive AI estimates are 10-20x too high. Focused engineering time is typically 5-10% of what AI predicts. Engify's MCP time estimator uses historical velocity for 10-20x more accurate estimates.
Plan → Build → Verify → Refactor. It's the core micro-cycle for AI-assisted development. Every task runs through PBVR with guardrails and memory.
Explore patterns, workflows, and guardrails for AI-augmented engineering teams.