Run big tasks and keep shipping

Learn how to tackle large, complex tasks by breaking them into parallel workstreams and delegating to multiple agents simultaneously — all while maintaining control and quality.

What This Video Covers

The Problem with Big Tasks

Complex projects naturally slow down development. A large refactoring, a multi-page website build, or a comprehensive test suite — these tasks take time, and traditional approaches often force you to wait for sequential completion:

One agent processes everything step by step
You’re blocked until the entire task completes
Context accumulates and eventually overflows
Progress feels slow and hard to track

This creates a frustrating bottleneck where ambitious work sits in progress for hours or days.

Breaking Down into Parallel Workstreams

The solution is to decompose large tasks into independent streams that can execute simultaneously. Instead of one agent doing everything:

Stream A handles the frontend components
Stream B builds the API endpoints
Stream C writes the database migrations
Stream D creates tests for the new features

Each stream is self-contained. They don’t depend on each other. They can all run at the same time.

The key skill is learning to identify natural boundaries in your work:

Task Type	How to Decompose
Multi-page website	One agent per page or section
Refactoring	One agent per module or component
Test coverage	One agent per test file or feature area
Research synthesis	One agent per source or topic
Documentation	One agent per section or topic

Swarm Mode: Parallel by Design

Swarm Mode is built specifically for this pattern. When you enable Swarm Mode:

The primary agent acts as an orchestrator
It analyzes your task and identifies parallel opportunities
It spawns subagents — one for each independent stream
All subagents run simultaneously, reporting progress in real time
Results are coordinated and integrated automatically

This isn’t just running multiple agents — it’s structured parallel execution with built-in coordination.

Managing Multiple Agents Without Losing Control

Running many agents in parallel raises a legitimate concern: How do you stay in control?

The answer lies in visibility and checkpoints:

The Agents Panel (Ctrl+3 or Alt+3) shows all active subagents, their status, and which files they’re working on. At a glance, you can see:

How many agents are running
What each agent is doing
Which agents have completed
Any errors or blocked agents

Foreground vs Background Execution:

Mode	When to Use
Foreground	You want to watch and guide in real time; agents share your workspace
Background	You’re delegating and will review later; uses git worktree on a separate branch

Checkpoint Decisions: Unlike fully autonomous systems, Swarm Mode lets you review before major integrations. The orchestrator can pause and ask for direction before combining streams that might conflict.

The Speed vs Coordination Trade-off

More parallel agents means faster execution — but only up to a point. There’s a trade-off:

Parallelism benefits:

Wall-clock time decreases dramatically
Different perspectives on the same problem
Natural fault isolation (one agent failing doesn’t block others)

Coordination costs:

More cognitive overhead to track progress
Potential for conflicting changes to the same files
Integration complexity when streams need to merge

The sweet spot depends on your task:

Highly independent streams (different pages, separate modules): Go wide — 4-8 agents
Somewhat related streams (different features in the same codebase): Moderate — 2-4 agents
Tightly coupled streams (refactoring shared core): Sequential — 1-2 agents

Practical Examples of Running Big Tasks

Example 1: Building a Multi-Feature Dashboard

Starting task: “Build a customer analytics dashboard with 5 different visualizations”

Swarm approach:

Agent 1: Authentication and routing
Agent 2: Data fetching layer and API integration
Agent 3: Chart component A (revenue metrics)
Agent 4: Chart component B (user activity)
Agent 5: Chart component C (conversion funnel)
Agent 6: Layout, navigation, and polish

Total time: 45 minutes parallel vs. 3+ hours sequential

Example 2: Migrating a Legacy API

Starting task: “Migrate our REST API to GraphQL”

Swarm approach:

Agent 1: Schema design for User endpoints
Agent 2: Schema design for Order endpoints
Agent 3: Resolvers for User queries
Agent 4: Resolvers for Order queries
Agent 5: Mutation implementations
Agent 6: Client-side query updates

The orchestrator coordinates the shared schema definition while subagents work in parallel on their domains.

Example 3: Content Production at Scale

Starting task: “Create 20 blog posts from this research material”

Swarm approach:

10 agents, each writing 2 posts
Each agent has access to the shared research folder
Posts are written in parallel and saved to individual files
Orchestrator can review and provide feedback in batches

Keeping Context and Continuity

A common concern: If each agent works independently, how do they stay consistent?

Shared Filesystem: All agents in a swarm share the same working directory. When Agent 1 creates a utility function, Agent 2 can import and use it. The filesystem becomes the shared context.

agent.md: Your project’s agent.md provides baseline context to every subagent. Define your coding standards, architecture patterns, and conventions once — all agents inherit them.

Plan as Contract: When you start from a Plan Mode session, the approved plan acts as a coordination contract. Every subagent knows the overall architecture and their specific role within it.

Incremental Integration: The orchestrator doesn’t wait for all agents to finish before starting integration. As agents complete their streams, results are integrated progressively, giving early visibility into how the pieces fit together.

Best Practices for Large-Scale Agent Workflows

1. Start with a Clear Plan

Don’t jump into Swarm Mode blindly. Use Plan Mode first to:

Understand the full scope
Identify the natural boundaries
Define interfaces between streams
Set expectations for each subagent

2. Keep Streams Independent

The more independent your streams, the smoother parallel execution will be:

Minimize shared files between agents
Define clear interfaces upfront
Avoid having multiple agents edit the same function

3. Use Background Mode for Large Swarms

When running 4+ agents on a significant task, consider Background mode:

Creates a clean git worktree on a new branch
Won’t clutter your main workspace timeline
Can check back later when everything is done
Easy to review the full diff before merging

4. Monitor the Agents Panel

Don’t just fire and forget. Check Ctrl+3 periodically:

See which agents are stuck or erroring
Spot potential conflicts early
Provide guidance to struggling agents
Celebrate when they all turn green

5. Iterate on Decomposition

Your first attempt at breaking down a task might not be optimal. That’s fine:

Some agents might finish much faster than others
You might discover hidden dependencies
Adjust the plan and respawn agents as needed

6. Maintain Quality Standards

Parallel execution shouldn’t mean lower quality:

Include a “review and test” stream in your swarm
Ask agents to write tests alongside their features
Use the orchestrator to enforce standards across all outputs

Key Takeaways

Decompose big tasks into parallel streams — look for natural boundaries where work can proceed independently
Use Swarm Mode for structured parallel execution — the orchestrator pattern provides coordination that ad-hoc parallelism lacks
Choose between foreground and background based on whether you want real-time visibility or clean separation
Balance speed and coordination — more agents isn’t always better; match parallelism to task independence
Let the filesystem be your shared context — agents stay consistent by reading and writing shared files
Ship continuously — parallel execution lets you make progress on big projects without blocking other work
Quality doesn’t require sequential execution — with proper planning and interfaces, parallel work meets the same standards

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