Parallel Agent Workflows

Introduction To Parallel Workflows!

In our previous lessons, we explored how to use specialized AI agents to handle complex tasks with high precision. We learned that by delegating work to subagents, we avoid context decay and keep code quality high. Now that you understand how to manage a single stream of work, it's time to look at how we can scale this process. In a production environment, we often have multiple features waiting to be built. If Feature A and Feature B do not rely on each other, we do not have to wait for Feature A to finish before starting Feature B . This is called Parallel Development . By running these workflows at the same time, we significantly reduce calendar time — the actual days or hours it takes to deliver the project — even if the total amount of work remains the same. In this lesson, you will learn how to identify when features can be built in parallel and how to coordinate them so they do not clash when they are merged back together.

Determining Feature Independence

Not every task can be done in parallel. If two features require changing the same line of code in the same file, they will cause a "conflict." To work in parallel, features must be independent . We use a simple checklist to verify independence: No shared files : Aside from basic configuration or test setup, the features should live in different files. No integration dependencies : Feature A should not need code from Feature B to function. Different database tables : They should not modify the same data structures. Different API endpoints : They should provide different routes for the user. Let's look at our target features: Task Tags and Task Reminders . Feature Tables Files Endpoints Task Tags tags, task_tags tag.py, tag_repository.py /tags Task Reminders reminders reminder.py, reminder_repository.py /reminders Since these use different tables and files, they are perfect candidates for parallel development. We can document this in a file called parallel-features-analysis.md to ensure our AI agents understand the boundaries.

Feature	Tables	Files	Endpoints
Task Tags	`tags`, `task_tags`	`tag.py`, `tag_repository.py`	`/tags`
Task Reminders	`reminders`	`reminder.py`, `reminder_repository.py`	`/reminders`

The 3-Phase Strategy At A Glance

With our features confirmed as independent, we can now apply a structured approach to building them in parallel. The diagram below illustrates the full workflow: a shared foundation is set first, two separate AI sessions then run concurrently, and a final integration phase ties everything together. The sections below walk through each phase in detail.

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Python

# database/migrations/versions/002_add_tags_and_reminders.py

def upgrade():
    # Create tables for Feature A: Tags
    op.create_table('tags', sa.Column('id', sa.Integer(), primary_key=True), sa.Column('name', sa.String()))
    op.create_table('task_tags', sa.Column('task_id', sa.Integer()), sa.Column('tag_id', sa.Integer()))

    # Create tables for Feature B: Reminders
    op.create_table('reminders', 
        sa.Column('id', sa.Integer(), primary_key=True), 
        sa.Column('task_id', sa.Integer()), 
        sa.Column('due_date', sa.DateTime()),
        sa.Column('description', sa.String())
    )

# database/migrations/versions/002_add_tags_and_reminders.py

def upgrade():
    # Create tables for Feature A: Tags
    op.create_table('tags', sa.Column('id', sa.Integer(), primary_key=True), sa.Column('name', sa.String()))
    op.create_table('task_tags', sa.Column('task_id', sa.Integer()), sa.Column('tag_id', sa.Integer()))

    # Create tables for Feature B: Reminders
    op.create_table('reminders', 
        sa.Column('id', sa.Integer(), primary_key=True), 
        sa.Column('task_id', sa.Integer()), 
        sa.Column('due_date', sa.DateTime()),
        sa.Column('description', sa.String())
    )

Shell

# Apply the migration
alembic upgrade head

# Verify that our Python models can see the new tables
python -c "from src.models.tag import Tag; from src.models.reminder import Reminder; print('Foundation OK')"

# Apply the migration
alembic upgrade head

# Verify that our Python models can see the new tables
python -c "from src.models.tag import Tag; from src.models.reminder import Reminder; print('Foundation OK')"

text

Foundation OK

text

Implement Task Tags.
Context: @specs/task-tags/tasks.md
Foundation: Tag and TaskTag models exist.
Tasks: T001 (Repository), T002 (Service), T003 (Endpoints).

Implement Task Tags.
Context: @specs/task-tags/tasks.md
Foundation: Tag and TaskTag models exist.
Tasks: T001 (Repository), T002 (Service), T003 (Endpoints).

text

Implement Task Reminders.
Context: @specs/task-reminders/tasks.md
Foundation: Reminder model exists.
Tasks: T001 (Repository), T002 (Service), T003 (Endpoints).

Implement Task Reminders.
Context: @specs/task-reminders/tasks.md
Foundation: Reminder model exists.
Tasks: T001 (Repository), T002 (Service), T003 (Endpoints).

Shell

# Create a new task
curl -X POST http://localhost:8000/api/tasks -d '{"title": "Parallel Test Task"}'

# Create a new task
curl -X POST http://localhost:8000/api/tasks -d '{"title": "Parallel Test Task"}'

text

{"id": 101, "title": "Parallel Test Task"}

{"id": 101, "title": "Parallel Test Task"}

Shell

# Add a tag
curl -X POST http://localhost:8000/api/tasks/101/tags -d '{"name": "urgent"}'

# Add a reminder
curl -X POST http://localhost:8000/api/tasks/101/reminders -d '{"due_date": "2024-12-31T10:00:00", "description": "Finish Lesson"}'

# Add a tag
curl -X POST http://localhost:8000/api/tasks/101/tags -d '{"name": "urgent"}'

# Add a reminder
curl -X POST http://localhost:8000/api/tasks/101/reminders -d '{"due_date": "2024-12-31T10:00:00", "description": "Finish Lesson"}'

Shell

curl http://localhost:8000/api/tasks/101

curl http://localhost:8000/api/tasks/101

JSON

{
  "id": 101,
  "title": "Parallel Test Task",
  "tags": [{"id": 1, "name": "urgent"}],
  "reminders": [{"id": 1, "description": "Finish Lesson", "due_date": "2024-12-31T10:00:00"}]
}

{
  "id": 101,
  "title": "Parallel Test Task",
  "tags": [{"id": 1, "name": "urgent"}],
  "reminders": [{"id": 1, "description": "Finish Lesson", "due_date": "2024-12-31T10:00:00"}]
}

Python

# database/migrations/versions/002_add_tags_and_reminders.py

def upgrade():
    # Create tables for Feature A: Tags
    op.create_table('tags', sa.Column('id', sa.Integer(), primary_key=True), sa.Column('name', sa.String()))
    op.create_table('task_tags', sa.Column('task_id', sa.Integer()), sa.Column('tag_id', sa.Integer()))

    # Create tables for Feature B: Reminders
    op.create_table('reminders', 
        sa.Column('id', sa.Integer(), primary_key=True), 
        sa.Column('task_id', sa.Integer()), 
        sa.Column('due_date', sa.DateTime()),
        sa.Column('description', sa.String())
    )

Shell

# Apply the migration
alembic upgrade head

# Verify that our Python models can see the new tables
python -c "from src.models.tag import Tag; from src.models.reminder import Reminder; print('Foundation OK')"

text

Foundation OK

text

Implement Task Tags.
Context: @specs/task-tags/tasks.md
Foundation: Tag and TaskTag models exist.
Tasks: T001 (Repository), T002 (Service), T003 (Endpoints).

text

Implement Task Reminders.
Context: @specs/task-reminders/tasks.md
Foundation: Reminder model exists.
Tasks: T001 (Repository), T002 (Service), T003 (Endpoints).

Shell

# Create a new task
curl -X POST http://localhost:8000/api/tasks -d '{"title": "Parallel Test Task"}'

text

{"id": 101, "title": "Parallel Test Task"}

Shell

# Add a tag
curl -X POST http://localhost:8000/api/tasks/101/tags -d '{"name": "urgent"}'

# Add a reminder
curl -X POST http://localhost:8000/api/tasks/101/reminders -d '{"due_date": "2024-12-31T10:00:00", "description": "Finish Lesson"}'

Shell

curl http://localhost:8000/api/tasks/101

JSON

{
  "id": 101,
  "title": "Parallel Test Task",
  "tags": [{"id": 1, "name": "urgent"}],
  "reminders": [{"id": 1, "description": "Finish Lesson", "due_date": "2024-12-31T10:00:00"}]
}

Introduction To Parallel Workflows!

Determining Feature Independence

The 3-Phase Strategy At A Glance

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Phase 1: Setting The Shared Foundation

Phase 2: Executing Parallel AI Sessions

Phase 3: Integration And Final Validation

Summary And Practice Overview