The AI-Driven Development Lifecycle

The structured process for
building software with AI.

AIDLC is an open methodology for engineering teams building with AI agents. Three phases. Defined roles. Mandatory approval gates. AI executes. Humans decide. Every step, by design.

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Most teams have AI tools. They don't have a process for AI.

AI-Assisted Development

Developers use copilots inside the same process they already had. AI speeds up individual tasks, but the workflow — how work is planned, decomposed, reviewed, and shipped — never changes. Coordination stays manual. Bottlenecks stay human. The team gets faster typing, not faster delivery.

AI-Autonomous Development

Teams point an agent at a ticket and let it run unsupervised. The AI builds confidently — and often builds the wrong thing. Without structured requirements, approval gates, and human checkpoints, autonomous AI creates expensive rework and erodes trust.

AIDLC is the third path. A structured lifecycle where AI proposes and executes, humans define intent and approve, and every artifact has a clear audit trail. Not assisted. Not autonomous. Collaborative by design.

The Pattern

One pattern. Repeats at every phase.

1

Human defines intent

A human describes what needs to happen and why — a business goal, a feature request, a deployment target. The intent is explicit and documented.

2

AI proposes

Based on the intent and structured context, the AI generates a concrete plan: requirements, a design, an implementation approach, a deployment strategy.

3

Human approves

A human reviews the proposal, asks questions, requests changes, and gives explicit approval. Nothing moves forward without a human decision.

4

AI executes. Human validates.

The AI carries out the approved plan. The human reviews the output, verifies it matches the intent, and signs off before the next step begins.

This loop is the foundation of every AIDLC phase. It creates consistency across Inception, Construction, and Operations — and ensures every AI-generated artifact is auditable, reviewable, and traceable back to a human decision.

The Lifecycle

Three phases. End to end.

Phase 1

Inception

Hours to days

Goal

Transform a business goal or feature request into structured, AI-ready requirements — a clear design, decomposed implementation units, and an audit log — so that Construction can begin with zero ambiguity.

Steps

  1. Human submits a work item with business intent.
  2. AI generates structured requirements (requirements.md).
  3. Human reviews and approves requirements.
  4. AI generates a design document (design.md).
  5. Human reviews and approves the design.
  6. AI decomposes the design into implementation units (units.md).
  7. Human reviews and approves units.

Artifacts

  • requirements.md
  • design.md
  • units.md
  • audit-log.md

Who drives

Product defines intent. AI generates artifacts. Engineering Lead reviews and approves.

Gate

All three artifacts (requirements, design, units) are approved by a human before Construction begins.

Phase 2

Construction

Days to weeks

Goal

Implement every unit from Inception — plan, code, test, and review — with AI executing and humans approving at every step.

Steps (per unit)

  1. AI generates an implementation plan for the unit.
  2. AI asks clarifying questions if needed.
  3. Human reviews and approves the plan.
  4. AI writes the code.
  5. Human reviews the code.
  6. AI writes tests.
  7. Human validates tests pass and reviews coverage.
  8. Human approves the unit for merge.

Artifacts

  • Implementation plan (per unit)
  • Source code and tests
  • Code review records
  • Updated audit-log.md

Who drives

Engineer owns each unit. AI executes plans. Engineering Lead reviews and approves.

Gate

Every unit passes code review and test validation before merge. All units complete before Operations begins.

Phase 3

Operations

Ongoing

Goal

Ship the completed work to production safely and maintain it — with AI-generated deployment plans, runbooks, and monitoring configurations, all human-approved.

Steps

  1. AI generates a deployment plan.
  2. Human reviews and approves the deployment plan.
  3. AI generates runbooks and monitoring configuration.
  4. Human reviews and approves operational docs.
  5. Team deploys to production.
  6. AI monitors and feeds lessons back into the knowledge layer.

Artifacts

  • deployment-plan.md
  • runbook.md
  • Monitoring and alerting configuration
  • Post-deployment review notes
  • Updated audit-log.md

Who drives

Engineering Lead owns deployment. AI generates plans and docs. Leadership reviews risk and approves release.

Gate

Deployment plan and runbook approved by a human before any production release.

How Context Works

The right context, loaded at the right time.

1. Org

Company-wide standards, coding conventions, security policies, and compliance requirements. Loaded into every AI interaction by default.

2. Team

Team-specific workflows, naming conventions, review standards, and tooling preferences. Scoped to the team running the lifecycle.

3. System

Architecture documentation, API contracts, dependency maps, and infrastructure topology for the system being built or modified.

4. Work Item

The specific business goal, requirements, design, and unit decomposition for the current piece of work.

5. Unit

The narrowest scope: a single implementation unit with its plan, code context, and test expectations.

Context is loaded progressively. At the Org level, the AI sees everything that applies globally. At the Unit level, it sees only what it needs to implement one focused piece of work. This prevents context overload and keeps AI output precise and relevant.

Who Does What

Five roles. Clear ownership. No ambiguity.

Product

Defines business intent and priorities. Submits work items. Validates that delivered work matches the original goal.

Engineering Lead

Reviews and approves all AI-generated artifacts — requirements, designs, plans, and code. Owns technical quality and process adherence.

Engineer

Owns individual units during Construction. Reviews AI-generated code, runs tests, and validates output before requesting approval.

Leadership

Reviews risk, approves production releases, and monitors organizational metrics. Accountable for process outcomes.

AI Agent

Generates plans, writes code, produces docs, asks clarifying questions. Never approves its own work. Always operates within human-defined boundaries.

What AIDLC is not.

Not AI-assisted.

AIDLC doesn't bolt AI onto an existing human process. It redesigns the process from the ground up so that AI and humans each do what they're best at — AI generates, humans decide.

Not AI-autonomous.

AIDLC never lets AI run unsupervised. Every AI-generated artifact requires explicit human approval before it moves forward. The human is always in the loop, by design.

Not rigid.

AIDLC defines phases, roles, and gates — but it doesn't prescribe tools, languages, or team structures. It's a methodology, not a straitjacket. Adapt it to your team, your stack, your pace.

Markdown and git. Always.

Every AIDLC artifact — requirements, designs, implementation plans, audit logs, deployment plans, runbooks — is a Markdown file stored in your git repository. Approvals are commits. Decisions are traceable in your git history. There is no proprietary database, no vendor lock-in, no black box.

AIDLC Workspace reads from and writes to your repos. If you stop using the Workspace, your entire process history — every requirement, every design decision, every approval — stays right where it belongs: in your codebase.

Ready to run AIDLC at your org?

Two paths forward — choose the one that fits.

AIDLC Workspace

Install the full lifecycle as a product. Onboard your repos, run sprints, and track results — all in one place.

See AIDLC Workspace

AIDLC Consulting

Work with our team to implement AIDLC at your organization. Workshops, 90-day implementations, and ongoing support.

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