Beginner

Introduction to Parallel Agents

Understand how running multiple AI agents simultaneously can dramatically reduce completion time for complex tasks, and where this pattern is used in practice.

What Are Parallel Agents?

Parallel agents are multiple AI agents that run simultaneously to speed up complex tasks. Instead of processing work sequentially — one step at a time — work is distributed across agents that run concurrently, like having multiple developers working on different parts of a project at the same time.

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Key distinction: Parallel agents are a specific execution pattern for sub agents. While sub agents can run sequentially (one after another), parallel agents run at the same time. This course focuses specifically on the concurrent execution pattern and its unique challenges.

Sequential vs Parallel Execution

The difference between sequential and parallel execution is dramatic, especially for tasks that can be decomposed into independent subtasks:

Sequential Execution (One at a time)

Task: Build 5 course sections

Agent 1: Build Section A  ... 3 min
          Agent 1: Build Section B  ... 3 min
                    Agent 1: Build Section C  ... 3 min
                              Agent 1: Build Section D  ... 3 min
                                        Agent 1: Build Section E  ... 3 min

Total time: ~15 minutes

Parallel Execution (All at once)

Task: Build 5 course sections

Agent 1: Build Section A  ... 3 min
Agent 2: Build Section B  ... 3 min
Agent 3: Build Section C  ... 3 min
Agent 4: Build Section D  ... 3 min
Agent 5: Build Section E  ... 3 min

Total time: ~3 minutes (5x faster!)

Speed Benefits

Parallel execution provides near-linear speedup when tasks are truly independent:

Agents	Sequential Time	Parallel Time	Speedup
2 agents	10 min	5 min	2x
5 agents	25 min	5 min	5x
10 agents	50 min	5 min	10x

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Reality check: In practice, speedup is slightly less than linear due to coordination overhead, shared resource contention, and the time needed to split and merge results. A 5-agent parallel system typically achieves 3-4x speedup, not a full 5x.

Real-World Examples

Here are practical scenarios where parallel agents provide significant value:

Building Course Content

Creating multiple HTML pages for a course simultaneously. Each agent writes a different lesson page, all following the same template and style guide.

Cross-Platform Testing

Running test suites across different environments simultaneously — one agent tests on Node 18, another on Node 20, and a third checks browser compatibility.

Multi-File Refactoring

When renaming a function used across 20 files, parallel agents can each handle a subset of files, completing the refactor in a fraction of the time.

Research and Analysis

Exploring different aspects of a large codebase simultaneously — one agent maps the API layer, another studies the database schema, a third reviews the auth system.

Where Parallel Agents Are Supported

Claude Code

Claude Code supports parallel agents through multiple Agent tool calls in a single message. When Claude determines that multiple independent tasks can run simultaneously, it invokes several Agent tools at once. Each agent runs in its own context and can optionally use a separate git worktree for file isolation.

Custom Implementations

You can build parallel agent systems in any programming language using:

Python asyncio: Use asyncio.gather() to run multiple API calls concurrently.
JavaScript Promise.all: Fire multiple agent calls and wait for all to complete.
Go goroutines: Leverage Go's native concurrency for agent orchestration.
Thread pools: Use threading in any language to parallelize blocking API calls.

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Important prerequisite: Before diving into parallel agents, make sure you understand sub agents — how they work, their types, and their lifecycle. Parallel agents build on those foundations and add the complexity of concurrent execution.

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