Step 1: Feature Breakdown and Decomposition

Start by decomposing your high-level features into smaller, manageable tasks or sub-features. This creates a granular view, making it easier to spot dependencies later. Use a structured approach like Work Breakdown Structure (WBS) from project management.

• List all features: Write down each major feature you want to implement (e.g., Feature A: User Authentication; Feature B: Payment Processing; Feature C: UI Dashboard).
• Break into sub-tasks: For each feature, divide it into atomic tasks. Focus on:
  • Functional components (e.g., backend API endpoints, database schemas, frontend components).
  • Non-functional aspects (e.g., error handling, testing, documentation).
  • Example: For "User Authentication":
    • Task 1: Define user model in database.
    • Task 2: Implement login endpoint.
    • Task 3: Add JWT token generation.
    • Task 4: Create frontend login form.
    • Task 5: Write unit tests for authentication logic.

Aim for tasks that are small enough to fit into a single PR (Pull Request) – ideally 1-2 days of work per task.

Step 2: Dependency Auditing

Audit the tasks to identify dependencies. This is like building a graph where tasks are nodes, and dependencies are directed edges (e.g., Task X must finish before Task Y).

• Map dependencies:

  • Data dependencies: Does one task produce data or artifacts needed by another? (e.g., Database schema must exist before API endpoints can use it.)
  • Logical dependencies: Does one task logically precede another? (e.g., Core logic before error handling.)
  • Resource dependencies: Shared resources like files, modules, or environments that could cause conflicts if modified simultaneously.
  • External dependencies: Third-party integrations, APIs, or hardware constraints.

• Tools for auditing:

  • Use a mind mapping tool (e.g., Miro, Lucidchart) or a simple spreadsheet to visualize.
  • Columns: Task ID, Description, Depends On (list of other Task IDs), Blocked By (reverse: what blocks this?).
  • Rows: One per task.

• Audit process:

  1. List all tasks in a table or list.
  2. For each task, ask: "What must be done before this can start?" and "What can be done in parallel?"
  3. Flag cyclic dependencies (e.g., A depends on B, B on A) – resolve by refactoring tasks.
  4. Categorize:
     • Independent: No dependencies (can start immediately).
     • Sequential: Strict order required.
     • Parallelizable: Can run concurrently if no shared mutable state.

Example table for auditing:

Task ID	Description	Depends On	Can Parallelize With
T1	Define DB schema	None	T4, T5
T2	Implement login endpoint	T1	None (sequential)
T3	Add JWT generation	T2	T4
T4	Create frontend form	None	T1, T3
T5	Write unit tests	T1, T2, T3	None

Step 3: Splitting into Independent Areas

Group tasks into "streams" or "branches" based on the audit. The goal is to maximize independence so you can assign them to separate PRs, threads, or even distributed systems.

• Identify independent clusters:

  • Look for tasks with no overlapping dependencies or shared resources.
  • Group by domain: e.g., Backend-only, Frontend-only, Testing-only.
  • Use graph theory basics: Find connected components (clusters of dependent tasks) and isolate them.
  • Rule: If two tasks touch the same code file/module without coordination, they can't be fully independent – use feature flags or branching to isolate.

• Splitting strategies:

  • Vertical splits: By layers (e.g., all database tasks in one stream, all UI in another).
  • Horizontal splits: By feature slices (e.g., end-to-end for one sub-feature, independent of others).
  • Modularization: Refactor code to reduce coupling (e.g., use interfaces or microservices patterns).
  • Aim for "embarrassingly parallel" tasks: Those that don't communicate or share state.

Step 4: Identifying Sequential vs. Parallel Paths

From the dependency graph:

• Sequential chains: Paths where tasks must happen in order (e.g., DB schema → API → Tests). These form critical paths – prioritize them to avoid bottlenecks.
• Parallel paths: Branches that can run simultaneously (e.g., Frontend UI while Backend APIs are built, as long as APIs are mocked).
• Calculate levels: Assign "levels" to tasks:
  • Level 0: No dependencies.
  • Level 1: Depends only on Level 0.
  • And so on. Parallelize all tasks at the same level.

Use topological sorting (e.g., via Kahn's algorithm) manually or with code:

• Start with tasks having no incoming dependencies.
• Remove them, update graph, repeat.

Step 5: Threading/Parallelizing the Work

Now, distribute the work for tools like Cursor (AI code gen), Auggie (assuming an agentic AI workflow, perhaps a typo for "Augie" or similar; treat as any AI agent).

• For multiple PRs:

  • Create separate Git branches for each independent cluster (e.g., feature/auth-db, feature/auth-frontend).
  • Use Cursor to generate code per branch: Prompt it with task descriptions, switching contexts via branches.
  • Merge sequentially only for dependent parts; use rebase for independents.

• For multi-threading/CPUs:

  • If using agentic flows (e.g., AI agents like Auto-GPT or similar), spawn multiple agents per independent task.
  • In code: Use Python's multiprocessing or threading for running Cursor/AI scripts in parallel (e.g., one process per task generating code).
  • Example: Script that queues independent tasks, spawns threads to call Cursor API/generate code, then integrates.

• For multiple computers:

  • Distribute via CI/CD pipelines (e.g., GitHub Actions with parallel jobs).
  • Use distributed task queues like Celery/RabbitMQ: Each computer runs an AI agent worker.
  • Sync via shared repo: Push generated code/PRs to a central Git repo.
  • Handle conflicts: Use locking (e.g., Git locks) or feature toggles to avoid merge hell.

• Generic agentic flow:

  1. Central orchestrator: A script/agent that audits dependencies and dispatches tasks.
  2. Dispatch: Send independent tasks to separate agents/instances (e.g., one Cursor instance per CPU/computer).
  3. Monitor: Poll for completion, then trigger dependents.
  4. Integrate: Auto-merge or human-review PRs.

Tips for Implementation

• Testing in parallel: Run unit tests per task, integration tests sequentially.
• Risks: Watch for race conditions in shared code; use mocks/stubs for dependencies.
• Scaling: Start small – test with 2-3 parallel streams before multi-computer.
• Tools integration: For Cursor, use its composer mode for per-task code gen. For agentic setups, chain with LangChain or similar to manage dependencies.

This method ensures efficient parallelization while minimizing errors from dependencies. Adapt based on your project's complexity.