Workflows

Named workflow patterns in AI-DLC - Default, TDD, Adversarial, and Hypothesis

Workflows

AI-DLC provides four named workflows, each optimized for different types of work. Choose the workflow that matches your task.

What Is a Workflow?

A workflow is a named sequence of hats. Each workflow defines:

  • Which hats are used
  • The order of transitions
  • The operating mode for each hat

Workflows are selected during /elaborate and can be customized per project.

Default Workflow

The standard development workflow for most feature work.

Hats

HatModeFocus
ElaboratorHITLDefine intent and criteria with user
PlannerHITLCreate tactical plan for the unit
BuilderOHOTLImplement according to plan
ReviewerHITLVerify implementation meets criteria

Flow

/elaborate
    ↓
Elaborator (HITL): Define what to build
    ↓
/construct
    ↓
Planner (HITL): Plan how to build it
    ↓
Builder (OHOTL): Build it
    ↓
Reviewer (HITL): Verify it's done
    ↓
Next unit or intent complete

When to Use

  • Standard feature development
  • General enhancements
  • Most CRUD operations
  • Integration work

Example

Intent: Add user profile editing

Elaborator: "What fields should users edit?"
You: "Name, email, avatar image"

Planner: "I'll add an /api/profile endpoint, a ProfileForm
component, and image upload with size validation."

Builder: [Implements the plan]

Reviewer: "All criteria met. Users can edit name, email,
upload avatar. Tests pass. Ready for review."

TDD Workflow

Test-Driven Development: Red-Green-Refactor pattern.

Hats

HatModeFocus
Test WriterOHOTLWrite failing tests first
ImplementerOHOTLMake tests pass with minimal code
RefactorerOHOTLImprove code while keeping tests green

Flow

/elaborate (select TDD workflow)
    ↓
/construct
    ↓
Test Writer (OHOTL): Write tests that fail
    ↓
Implementer (OHOTL): Make tests pass
    ↓
Refactorer (OHOTL): Clean up the code
    ↓
All tests still pass? → Next unit

When to Use

  • Well-specified behavior (API contracts, business rules)
  • Bug fixes (write test that reproduces, then fix)
  • Refactoring with safety net
  • When you want tests as living documentation

Example

Intent: Fix payment calculation bug

Test Writer: "Writing test that reproduces the bug:
  test('applies discount before tax, not after')"

Implementer: "Test fails as expected. Fixing calculation
order in applyDiscount()... Test passes."

Refactorer: "Extracting discount logic to separate
function for clarity. All tests still green."

Philosophy

"Write the tests you wish you had."

The Test Writer doesn't just write any tests - it writes the tests that would have caught the bug or validated the feature from the start.

Adversarial Workflow

Security-focused development with Red/Blue team phases.

Hats

HatModeFocus
BuilderOHOTLImplement to specification
Red TeamOHOTLAttack - find vulnerabilities
Blue TeamOHOTLDefend - fix vulnerabilities
ReviewerHITLFinal security review

Flow

/elaborate (select adversarial workflow)
    ↓
/construct
    ↓
Builder (OHOTL): Implement the feature
    ↓
Red Team (OHOTL): Try to break it
    ↓
Blue Team (OHOTL): Fix what was found
    ↓
Reviewer (HITL): Final security validation
    ↓
Secure? → Complete

When to Use

  • Authentication/authorization features
  • Payment processing
  • Data handling with privacy implications
  • API endpoints exposed to public
  • Features handling sensitive data

Example

Intent: Add API key management

Builder: "Implemented API key generation, storage,
and validation endpoints."

Red Team: "Found issues:
1. Keys stored in plaintext (should hash)
2. No rate limiting on key validation
3. Timing attack possible on key comparison"

Blue Team: "Fixed all issues:
1. Now using bcrypt for key hashes
2. Added rate limiting (100/min)
3. Using constant-time comparison"

Reviewer: "Verified fixes. Security scan passes.
Approved for production."

Philosophy

The adversarial workflow assumes every feature is a potential attack surface. By switching between attacker and defender mindsets, you build more secure software.

Hypothesis Workflow

Scientific debugging for investigating complex bugs.

Hats

HatModeFocus
ObserverOHOTLGather data about the bug
HypothesizerHITLForm theories about the cause
ExperimenterOHOTLTest hypotheses systematically
AnalystHITLEvaluate results and implement fix

Flow

/elaborate (select hypothesis workflow)
    ↓
/construct
    ↓
Observer (OHOTL): Collect symptoms, logs, traces
    ↓
Hypothesizer (HITL): Form ranked hypotheses
    ↓
Experimenter (OHOTL): Test each hypothesis
    ↓
Analyst (HITL): Interpret results, implement fix
    ↓
Bug fixed? → Complete

When to Use

  • Intermittent/flaky bugs
  • Performance issues with unknown cause
  • Production incidents
  • Bugs that "shouldn't happen"
  • Complex system interactions

Example

Intent: Fix intermittent 500 errors on checkout

Observer: "Gathered data:
- Errors occur ~2% of requests
- More frequent during peak hours
- Stack trace shows DB timeout
- No correlation with specific users"

Hypothesizer: "Ranked hypotheses:
1. Connection pool exhaustion under load
2. Slow query blocking connections
3. Database resource contention
4. Network latency spikes"

Experimenter: "Testing hypothesis #1:
- Added connection pool metrics
- Found: pool exhausted at 25 connections
- Peak requires ~40 connections"

Analyst: "Root cause confirmed. Fix:
- Increased pool size to 50
- Added connection timeout handling
- Monitoring shows 0% error rate for 24h"

Philosophy

"Don't guess - investigate."

The hypothesis workflow prevents "shotgun debugging" (changing random things hoping to fix the bug). Each change is a controlled experiment testing a specific hypothesis.

Choosing a Workflow

Task TypeWorkflowWhy
New featureDefaultBalanced plan-build-review cycle
Bug fix (known cause)TDDTest reproduces bug, verifies fix
Bug fix (unknown cause)HypothesisSystematic investigation
Security-sensitiveAdversarialBuilt-in security validation
Performance workHypothesisData-driven optimization
RefactoringTDDTests provide safety net

Custom Workflows

Create project-specific workflows in .ai-dlc/workflows.yml:

workflows:
  research-first:
    description: Research before building
    hats: [elaborator, researcher, planner, builder, reviewer]

  design-heavy:
    description: UX-focused with design phase
    hats: [elaborator, designer, builder, reviewer]

  quick-fix:
    description: Minimal overhead for trivial changes
    hats: [builder, reviewer]

Next Steps