AI-DLC vs Agile

Side-by-Side Comparison

Aspect	Agile/Scrum	AI-DLC
Iteration Duration	Weeks (Sprints)	Hours/Days (Bolts)
Who Drives	Human-driven, AI assists	AI-driven, human-validated
Design Techniques	Out of scope	Integrated (DDD in construction bolts)
Task Decomposition	Manual	AI-powered
Phases	Repeating sprints	Rapid three-phase cycles
Rituals	Daily standups, retrospectives	Mob Elaboration, Mob Construction
Documentation	Often neglected	Built-in artifacts
Context Engineering	Lost between sprints	Specs + Memory Bank

Iteration: Sprints vs Bolts

Agile Sprints
AI-DLC Bolts

Duration: 1-4 weeks
Planning: Sprint planning ceremony
Execution: Daily standups, continuous work
Review: Sprint review, retrospective
Output: Potentially shippable increment

Role Inversion

Traditional Agile

Humans drive the entire process. AI tools (copilots, assistants) help with specific tasks but don’t lead.

AI-DLC

AI leads the conversation. Humans provide intent and validation. AI handles decomposition, planning, and execution.

Design Techniques

Agile Approach

Design techniques like DDD are optional. Many teams skip them due to time pressure, leading to technical debt.

AI-DLC Approach

Domain-Driven Design is built into DDD construction bolts. You can’t skip Domain Modeling - it’s a required gate.

Bolt Types with Built-in Design

Bolt Type	Best For	Stages
DDD Construction	Complex domain logic, business rules	Model → Design → ADR → Implement → Test
Simple Construction	UI, integrations, utilities	Plan → Implement → Test

Context Engineering

One of the biggest challenges in traditional Agile is context loss between sprints. Knowledge leaves with team members, decisions aren’t documented, and the codebase becomes a mystery.

AI-DLC Solution: Specs + Memory Bank

Specs and Memory Bank provide structured context for AI agents:

All project artifacts (requirements, designs, decisions)
Traceability between artifacts
Context that agents can reload in any session

memory-bank/
├── intents/           # What we're building
├── bolts/             # How we built it
├── standards/         # Project decisions
└── operations/        # Deployment context

When to Use Each

Use Agile When
Use AI-DLC When

Team is not using AI coding tools
Organization has established Agile processes
Regulatory requirements mandate specific ceremonies
Team prefers human-led planning

Migration Path

AI-DLC retains familiar concepts to ease transition:

Agile Concept	AI-DLC Equivalent
Epic	Intent
User Story	Story (within Unit)
Sprint	Bolt
Backlog	Intent/Unit definitions
Definition of Done	Checkpoint validations

Getting Started

Framework

Ideation Flow

FIRE Flow

AI-DLC Flow

Simple Flow

Compare

Resources

Side-by-Side Comparison

Iteration: Sprints vs Bolts

Role Inversion

Traditional Agile

AI-DLC

Design Techniques

Agile Approach

AI-DLC Approach

Bolt Types with Built-in Design

Context Engineering

AI-DLC Solution: Specs + Memory Bank

When to Use Each

Migration Path

​Side-by-Side Comparison

​Iteration: Sprints vs Bolts

​Role Inversion

​Traditional Agile

​AI-DLC

​Design Techniques

Agile Approach

AI-DLC Approach

​Bolt Types with Built-in Design

​Context Engineering

​AI-DLC Solution: Specs + Memory Bank

​When to Use Each

​Migration Path

Side-by-Side Comparison

Iteration: Sprints vs Bolts

Role Inversion

Traditional Agile

AI-DLC

Design Techniques

Bolt Types with Built-in Design

Context Engineering

AI-DLC Solution: Specs + Memory Bank

When to Use Each

Migration Path