## The phases, upgraded with AI

The six-phase lifecycle that software teams have used for decades — plan, design, implement, test, review, maintain — is still intact. What's changed is that each phase now has an AI participant alongside the human one. The nature of that participation varies by phase, but the effect compounds across the full cycle.

Plan
AI helps draft specs, break down tasks, and propose architectures.
Design
AI proposes architectures, evaluates implementation patterns, and scaffolds system structure.
Implement
Agents like Cursor, Claude Code, and Copilot generate code, refactor, and scaffold modules — at a pace no human team can match alone.
Test
AI writes tests, fuzzes inputs, and reasons about edge cases.
Review
LLMs summarize diffs, flag inconsistencies, and enforce patterns — though not architectural invariants.
Maintain
AI assists with migrations, dependency updates, and documentation.

The lifecycle remains familiar. What changes is the speed, scale, and autonomy of code generation — and the constraint surface that has to span all six phases instead of living in the review step.

The AI SDLC · six phases with an AI participant · governance spans all of them
Plan
specs, breakdown
Design
architectures, scaffolds
Implement
generation at velocity
Test
cases, fuzzing
Review
diffs, patterns
Maintain
migrations, docs
Governance layer Architectural decisions, scope, precedence, pre-generation enforcement — applied across every phase, not just at review
Each phase keeps its traditional name. Each phase now has an AI participant. The governance bar across the bottom is the new layer the lifecycle requires to remain coherent at AI velocity.

## Why the term exists

AI SDLC gives teams a common frame for a complex transition:

"We're modernizing the SDLC for AI-native engineering."

It's a category label — a way to talk about the shift without requiring everyone to understand architectural governance concepts first. The term maps an unfamiliar problem onto a familiar frame, which makes it useful for alignment across engineering, product, and leadership. That's why it travels: it meets people where they are before it asks them to think differently about where they're going.

The risk is that teams stop at the label. Adopting AI tooling across the SDLC without adapting the governance model that sits underneath it is how architectural drift becomes a structural problem rather than a sprint retrospective item.

## Where Mneme fits

Mneme isn't redefining the SDLC. Mneme is defining the governance layer required because AI is now part of the SDLC.

When AI agents participate in implementation and decision-making across the lifecycle, they generate code at a speed and volume that outpaces traditional quality controls. Review stays linear. Architecture gets complicated faster than it gets documented. Decisions made in one session don't carry forward into the next.

Agent-side governance
Injects architectural constraints before generation — so the AI writes within your decision boundaries, not around them.
Deterministic enforcement in CI
Validates architectural compliance before merge — a hard gate, not a suggestion.
Benchmarks and ADR enforcement
Maintains architectural continuity over time, across tools, and across engineers.

The AI SDLC introduced a new systems problem: architectural drift at machine speed. Mneme exists to govern that layer.

As AI coding agents become infrastructure, architectural governance becomes part of the software lifecycle itself.

## Related reading

- Bain’s AI Development Lifecycle Report Reveals the Next Challenge: Governance
- Deployment Quality Will Define the AI Era
- Review Is Not Governance
- Architectural Governance Across Heterogeneous AI Coding Agents

## FAQ

Is the AI SDLC a new methodology like Agile or DevOps?
No. Agile changed how teams organize work. DevOps changed how code moves from development to production. AI SDLC describes a shift in who — or what — participates in the existing lifecycle. The phases remain the same; the participants and the throughput change. What that participation requires operationally is the governance conversation, not a new process framework.
Does AI benefit every SDLC phase equally?
No. The implementation phase sees the most immediate impact — agents can generate, refactor, and scaffold code at a pace that compresses timelines significantly. Planning and design benefit from AI as a thought partner, but remain human-judgment-heavy. Review and maintain phases are where the downstream risk concentrates: if implementation accelerates but review stays linear, the backlog of architectural decisions grows faster than it's resolved. That's the core structural tension in the AI SDLC.
What goes wrong if you skip governance in the AI SDLC?
Architectural drift. AI agents generate code that's syntactically correct and locally reasonable, but that accumulates decisions inconsistent with the broader architecture — wrong storage layers, dependency violations, pattern deviations — at a rate no review process can absorb. The problem is invisible until the codebase becomes expensive to change. See Why Code Review Cannot Scale With AI Output for the throughput math, and Review Is Not Governance for why catching it at PR time is the wrong layer.
How does Mneme fit into an existing CI/CD pipeline?
Mneme operates at two points. At generation time, it injects relevant architectural decision records into the agent's context via tool hooks (Claude Code's PreToolUse, Cursor's rules export). At commit time, the CI gate validates the output against the same decision corpus and either warns or blocks, depending on configuration. It doesn't replace CI/CD — it adds an architectural compliance layer that runs before your existing quality gates. See the GitHub Actions integration for setup details.
ï»¿