AI-DLC vs Agile

How AI-DLC compares to traditional Agile methods

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

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

AI-DLC Bolts

• Duration: Hours to days
• Planning: AI-powered decomposition
• Execution: Stage-gated progression
• Review: Human validation at each checkpoint
• Output: Validated, tested feature

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

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

Use AI-DLC When

• Building with AI coding assistants
• Need rapid iteration cycles
• Want integrated design practices
• Building complex systems
• Context persistence is critical

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