ai-dlc-mode-selection

name	ai-dlc-mode-selection
description	Use when deciding between HITL, OHOTL, and AHOTL modes in AI-DLC workflows. Covers decision frameworks for human involvement levels and mode transitions.
allowed-tools	["Read","Grep","Glob"]

AI-DLC Mode Selection

AI-DLC supports three modes of human-AI collaboration. Choosing the right mode for each phase of work is critical for balancing productivity, quality, and control.

The Three Modes

HITL - Human In The Loop

Human actively participates in every decision.

Characteristics:

• Every action reviewed by human
• Synchronous collaboration
• Human makes final decisions
• AI proposes, human disposes

When to Use:

• Defining requirements (elaboration)
• Making architectural decisions
• Reviewing security-sensitive changes
• Course corrections when AI is off track

Examples:

User: "Add user authentication"
AI: "What authentication method? OAuth, email/password, or both?"
User: "Start with email/password, we'll add OAuth later"
AI: "Should we support 'remember me'?"
User: "Yes, 30-day sessions"

OHOTL - Occasional Human Over The Loop

Human sets direction, AI operates with periodic checkpoints.

Characteristics:

• AI works autonomously on defined tasks
• Human reviews at milestones
• Human intervenes when stuck or for approval
• Balance of autonomy and oversight

When to Use:

• Building well-defined features
• Tasks with clear completion criteria
• When backpressure provides quality gates
• Medium-complexity work

Examples:

User: "Implement the login form based on these criteria"
AI: [Works autonomously]
AI: "Login form complete. Tests passing. Ready for review."
User: "Looks good, continue to the API integration"

AHOTL - Autonomous Human Over The Loop

AI operates with minimal human involvement.

Characteristics:

• AI makes most decisions independently
• Human reviews only at completion or on exception
• Requires very clear criteria and robust backpressure
• Maximum autonomy

When to Use:

• Well-defined, routine tasks
• Tasks with comprehensive test coverage
• When all edge cases are known
• Low-risk changes

Examples:

User: "Implement all the CRUD endpoints for the User model"
AI: [Completes multiple iterations autonomously]
AI: "All endpoints implemented. 47 tests passing. PR ready."
User: "Merged."

Mode Selection Framework

Decision Matrix

Factor	HITL	OHOTL	AHOTL
Requirements clarity	Low	Medium	High
Risk level	High	Medium	Low
Test coverage	Low	Medium	High
Domain familiarity	Low	Medium	High
Reversibility	Difficult	Moderate	Easy

Questions to Ask

1. How clear are the requirements?

   • Vague → HITL
   • Mostly clear → OHOTL
   • Crystal clear → AHOTL

2. What's the risk of mistakes?

   • Security/data loss → HITL
   • User-facing bugs → OHOTL
   • Internal tooling → AHOTL

3. How good is test coverage?

   • No tests → HITL
   • Some tests → OHOTL
   • Comprehensive tests → AHOTL

4. How familiar is the domain?

   • New/complex domain → HITL
   • Familiar patterns → OHOTL
   • Routine work → AHOTL

5. How reversible are changes?

   • Database migrations → HITL
   • API changes → OHOTL
   • Internal refactoring → AHOTL

Mode by Phase

Default Workflow Modes

Phase	Default Mode	Rationale
Elaboration	HITL	Requires human input for requirements
Planning	HITL	Human should validate approach
Building	OHOTL	Autonomous with backpressure
Review	HITL	Human verification before completion

Mode Overrides

You can override defaults in .ai-dlc/hats.yml:

hats:
  builder:
    mode: AHOTL  # Override to full autonomy
    instructions: |
      Work autonomously. Only stop if blocked.

Transitioning Between Modes

Upgrading Autonomy (HITL → OHOTL → AHOTL)

When to upgrade:

• Requirements have stabilized
• Test coverage is comprehensive
• Pattern is established
• Human has built trust

Example:

Session 1 (HITL): Define auth requirements together
Session 2 (HITL): Review initial implementation
Session 3 (OHOTL): AI implements remaining endpoints
Session 4 (AHOTL): AI handles routine CRUD operations

Downgrading Autonomy (AHOTL → OHOTL → HITL)

When to downgrade:

• Unexpected complexity discovered
• AI making repeated mistakes
• Security concerns arise
• Requirements changed

Example:

AI operating in AHOTL...
AI: "I'm stuck on edge case X. Need clarification."
→ Downgrade to HITL for this issue
→ Resume OHOTL once resolved

Mode Indicators

Signs You're in the Wrong Mode

Too much autonomy (should downgrade):

• Repeated mistakes on similar issues
• Misunderstanding requirements
• Missing edge cases
• User frequently correcting course

Too little autonomy (should upgrade):

• User rubber-stamping every decision
• Routine, repetitive work
• Comprehensive test coverage exists
• AI consistently making good decisions

Calibration Questions

Ask periodically:

• "Am I making decisions the human should make?"
• "Am I asking for approval on routine choices?"
• "Are my autonomous decisions causing rework?"
• "Is the human adding value at this checkpoint?"

Mode-Specific Behaviors

In HITL Mode

- Ask before every significant decision
- Present options with trade-offs
- Wait for explicit approval
- Document decisions with rationale

In OHOTL Mode

- Make routine decisions autonomously
- Check in at milestones
- Ask when genuinely uncertain
- Save progress frequently (han keep)

In AHOTL Mode

- Make all decisions within criteria bounds
- Only interrupt for true blockers
- Log decisions for later review
- Complete full task before seeking feedback

Backpressure by Mode

HITL Backpressure

Human IS the backpressure:

• Every change reviewed
• Human catches issues immediately
• No automated gates needed

OHOTL Backpressure

Mix of automated and human:

• Automated: tests, lint, types
• Human: milestone reviews, PR approval
• AI operates freely within automated bounds

AHOTL Backpressure

Fully automated:

• Comprehensive test suite
• Strict type checking
• Automated code review tools
• CI/CD pipeline as final gate

Examples

Example 1: Security Feature

phase: authentication
mode: HITL  # Security-sensitive
reason: |
  Authentication has security implications.
  Every decision needs human review.

Example 2: UI Component

phase: component_library
mode: OHOTL  # Balanced
reason: |
  Design system is established.
  AI implements, human reviews at milestones.

Example 3: Data Migration Script

phase: migration
mode: HITL  # High risk
reason: |
  Database changes are difficult to reverse.
  Human must verify each step.

Example 4: Unit Tests

phase: test_writing
mode: AHOTL  # Low risk
reason: |
  Tests are additive and easily reversible.
  Existing tests validate correctness.

Summary

Mode	Human Involvement	Use When
HITL	Every decision	High risk, unclear requirements
OHOTL	At milestones	Medium risk, clear criteria
AHOTL	At completion	Low risk, comprehensive tests

Default rule: Start with HITL for new work, upgrade autonomy as trust builds and tests accumulate.