Decomposing Specs into Actionable Tasks with AI
Decomposing a large specification into smaller, actionable tasks is the bridge between vision and execution. A spec might state "Build a user authentication service with OAuth, email verification, and multi-factor authentication"—valid at a high level, but an AI agent cannot implement all three simultaneously. Decomposition breaks that spec into concrete, self-contained tasks: "Implement email verification flow," "Add TOTP MFA support," "Validate OAuth token endpoints," each with its own mini-spec, success criteria, and dependencies. This article teaches the art and science of spec decomposition for AI-driven development.
Why Decomposition Matters
A spec that's too large creates several problems:
- Ambiguous scope: AI agents generate code that's either incomplete or over-engineered, because they don't know where to stop.
- Hard to verify: A 5,000-line generated file is difficult to review and test comprehensively.
- No parallelism: Multiple AI agents or humans cannot work on the same spec simultaneously without conflicts.
- Unclear priorities: Which parts are critical path? Which can be deferred?
Decomposition solves these by creating a task graph: small, verifiable units with explicit dependencies and clear success criteria.
Decomposition Strategies
Strategy 1: By Module or Domain
Break the spec along architectural boundaries—each module owns a domain:
Authentication Service (spec)
├── Email Verification Module
│ ├── Task: Implement email validation regex and normalization
│ ├── Task: Implement OTP generation (6-digit, 15-min expiry)
│ ├── Task: Implement email sending (SMTP integration)
│ └── Task: Implement verification endpoint
├── Password Management Module
│ ├── Task: Implement bcrypt password hashing
│ ├── Task: Implement password reset flow (token, email link)
│ └── Task: Implement password strength validation
└── OAuth Module
├── Task: Implement OAuth discovery endpoint
├── Task: Implement authorization code flow
└── Task: Implement token endpoint
Each task has its own mini-spec. For example, "Task: Implement OTP generation":
Task: Generate One-Time Passwords (OTP)
Description: |
Generate a secure, time-based OTP for email verification.
Input Spec:
- None (side effect: writes to database)
Output Spec:
- Returns 6-digit numeric string
- Valid for 15 minutes from generation
- Each request generates a new OTP
Success Criteria:
- Generated OTP is cryptographically random
- OTP cannot be reused
- OTP stored salted in database
Dependencies:
- Database schema includes 'otp' table with fields: code, email, created_at, expires_at
Test Vectors:
- Generate 100 OTPs, verify all are unique
- Verify OTP is invalid after 15 minutes
- Verify database contains hashed OTP
Strategy 2: By User Workflow
Decompose along the happy path and error paths:
User Registration Workflow (spec)
├── Happy Path
│ ├── Task: Accept email and password
│ ├── Task: Validate inputs (format, length)
│ ├── Task: Send verification email
│ ├── Task: Verify email (via link or code)
│ └── Task: Create account in database
└── Error/Edge Paths
├── Task: Handle duplicate email
├── Task: Handle weak password
├── Task: Handle email delivery failure
└── Task: Handle expired verification token
This makes error handling explicit and testable.
Strategy 3: By Criticality and Risk
Prioritize by impact and uncertainty:
Payment Service (spec)
├── Critical Path (implement first)
│ ├── Task: Validate payment amount
│ ├── Task: Charge credit card (call payment processor)
│ └── Task: Record transaction in ledger (immutable)
├── High-Value (implement second)
│ ├── Task: Implement idempotency keys (prevent double-charge)
│ ├── Task: Implement refund workflow
│ └── Task: Implement transaction status webhook
└── Nice-to-Have (implement last)
├── Task: Implement payment analytics dashboard
└── Task: Implement fraud detection rules
Decomposing by criticality ensures that high-risk work is reviewed early and thoroughly.
Creating Task Descriptions for AI
Once decomposed, each task needs a precise description. A vague task description ("Implement OTP") leads to unpredictable code. A precise one guides AI accurately:
TASK: Generate One-Time Password (OTP) for Email Verification
Objective:
Generate a 6-digit numeric OTP that the user receives via email to verify
their email address during account creation.
Input:
- email: string (already validated as RFC 5322 format)
Output:
- otp: string (exactly 6 numeric digits, e.g., "123456")
- expiresAt: Unix timestamp (current time + 15 minutes)
Constraints:
- OTP must be cryptographically random
- OTP must be unique (not previously issued to this email)
- OTP must be stored hashed in the database (not plaintext)
- OTP cannot be reused for multiple verification attempts
- OTP expires after 15 minutes
Error Handling:
- If email has exceeded 5 OTP requests in last hour, reject with 429 Too Many Requests
- If database write fails, raise DatabaseError (caller retries)
Dependencies:
- Module: database (assumes 'otp' table exists)
- Module: crypto (for random number generation and hashing)
Testing:
- Verify 1000 generated OTPs are unique
- Verify OTP is rejected after 15 minutes
- Verify OTP hash in database cannot be reversed to plaintext
- Verify rate limiting (5 per hour) is enforced
This task description is structured, testable, and gives an AI agent clear boundaries.
Dependency Mapping
A critical step is mapping task dependencies. A dependency graph ensures tasks can be parallelized safely:
Email Validation Regex (Task 1) ← no dependencies
↓
Email Verification Endpoint (Task 2, depends on Task 1)
↓
Send Verification Email (Task 3, depends on Task 2 + SMTP setup)
↓
Verify Token in Email (Task 4, depends on Task 3)
Password Hashing (Task 5, parallel to Task 1–4)
↓
Password Reset Endpoint (Task 6, depends on Task 5)
With this graph, you can assign Task 1 and Task 5 to different AI agents in parallel, then Task 2 once Task 1 completes, and so on. This dramatically speeds up development.
Example: Decomposing an E-Commerce Spec
Here's a realistic example of decomposing a product catalog specification:
Product Catalog Service (High-Level Spec)
├── Core Data Model (foundational)
│ └── Task: Define Product, Category, Inventory schemas
├── Query APIs (primary workflow)
│ ├── Task: Implement GET /products (paginated, searchable)
│ ├── Task: Implement GET /products/{id} (single product)
│ └── Task: Implement GET /categories (list categories)
├── Search and Filtering (secondary)
│ ├── Task: Implement text search (name, description)
│ ├── Task: Implement filter by category, price range, rating
│ └── Task: Implement sorting (name, price, rating)
├── Admin APIs (restricted)
│ ├── Task: Implement POST /products (create product, requires auth)
│ ├── Task: Implement PATCH /products/{id} (update, requires auth)
│ └── Task: Implement DELETE /products/{id} (delete, requires auth)
└── Performance & Monitoring
├── Task: Add caching (Redis) for popular products
├── Task: Add request logging and metrics
└── Task: Add API rate limiting
Each task has its own verification criteria. For example, "Implement GET /products (paginated, searchable)" might have these success criteria:
✓ Returns 20 products per page (default)
✓ Accepts page and limit query parameters
✓ Total product count matches database
✓ Results are ordered by creation date (newest first)
✓ Search query matches product name/description using substring match
✓ Search is case-insensitive
✓ Response time < 200ms for 100K products
✓ Handles missing page parameter (defaults to 1)
Using AI to Decompose Specs Automatically
You can prompt an AI to decompose a spec for you:
Decompose this specification into 8-15 atomic tasks suitable for
AI code generation:
Specification: Build a real-time chat service with the following features:
- User authentication (JWT)
- Create, list, and archive chat rooms
- Send and receive messages in real-time (WebSocket)
- Store message history in database
- Admin dashboard for moderation
For each task, provide:
1. Task name
2. 1-2 sentence description
3. Acceptance criteria (bullet list)
4. Dependencies (what must be done first)
5. Estimated complexity (low/medium/high)
Format output as JSON.
An AI can analyze the spec and produce a structured task list, which humans then review and refine.
Comparison Table: Decomposition Approaches
| Approach | Best For | Parallelizable | Complexity | Learning Curve |
|---|---|---|---|---|
| By module | Large services, clear architecture | High | Medium | Low |
| By workflow | User-facing features, UX-heavy | Medium | Medium | Low |
| By criticality | Risk mitigation, phased rollout | Low | High | Medium |
| Hybrid (combo) | Complex projects with many concerns | High | High | Medium |
Key Takeaways
- Decomposing specs into tasks prevents AI agents from becoming overwhelmed and generating incomplete or bloated code.
- Clear task descriptions, dependencies, and success criteria allow multiple agents (human or AI) to work in parallel.
- Dependency graphs reveal critical paths and enable realistic scheduling.
- Task-level specs are easier to verify than monolithic specs; they support incremental development and testing.
- AI can assist in decomposition, but humans must validate the task graph for correctness and completeness.
Frequently Asked Questions
How small should a task be?
A good task is completable in 4–8 hours of focused work (for humans or AI). If a task takes more than 24 hours, decompose further. If it takes less than 2 hours, consider combining with related tasks.
What if two tasks have a circular dependency?
Circular dependencies are a design flaw. Refactor to break the cycle. For example, if TaskA depends on TaskB and TaskB depends on TaskA, introduce an abstraction or intermediate layer.
How do I handle dependencies on external services?
Mock or stub external services during development. For example, if TaskX depends on "payment processor integration," create a mock payment service that returns fixed responses. Real integration becomes a separate task done after all local tasks.
Should I decompose before or after writing the main spec?
Both. Start with a rough decomposition as you write the main spec. Refine the decomposition after the spec is complete and reviewed. Iteration is normal.