The 3 Phases of the AI-Driven Development Lifecycle

The AI-Driven Development Lifecycle (AIDLC) structures every phase of software development for teams building with AI agents.

Here's a walkthrough of each phase — what happens, who owns it, and what the approval gate looks like.

Phase 1: Inception

Goal: Understand intent, design solution, plan execution.

The Inception phase transforms a vague idea into structured, agent-ready work items. Product defines the intent statement. Requirements are elaborated with AI assistance. The Engineering Lead approves the design. Work is broken into independently buildable units.

Gate: Nothing moves to construction until the requirements PR is merged.

Phase 2: Construction

Goal: Build, test, and validate per unit.

Engineers claim units with full context already loaded — from the original intent statement through the approved design to the unit-level spec. AI generates code from the approved design. Every checkpoint has an approval gate.

Gate: Unit is done when it builds, tests pass, and PR is merged.

Phase 3: Operations

Goal: Deploy, monitor, maintain.

AI generates deployment plans and rollback procedures before anything ships. Every incident response follows the same pattern: AI proposes, human approves. The audit trail is the same git log that tracked every Inception decision.

Gate: Deployment readiness check before anything ships.

The Pattern That Repeats

At every phase, the same mechanic runs:

1. Human defines intent
2. AI proposes a plan and asks clarifying questions
3. Human approves
4. AI executes, human validates

This consistency is what makes AIDLC auditable and repeatable at scale.

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