Lilly Tech Systems
Learn Parallel Agents
Run multiple AI agents simultaneously to dramatically speed up complex tasks. Learn concurrent execution patterns, fan-out/fan-in architectures, and how to safely parallelize work across agents.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What are parallel agents? Compare sequential vs parallel execution and understand the speed benefits.
2. Architecture
Fan-out/fan-in patterns, task decomposition, shared state management, and error handling strategies.
3. Patterns
Map-reduce, pipeline parallelism, scatter-gather, worker pools, and anti-patterns to avoid.
4. Implementation
Build parallel agents in Claude Code, Python asyncio, and JavaScript Promise.all with worktree isolation.
5. Best Practices
Task decomposition strategies, optimal parallelism levels, cost vs speed tradeoffs, and failure handling.
What You'll Learn
By the end of this course, you'll be able to:
Design Parallel Workflows
Decompose complex tasks into independent units that can be processed simultaneously by multiple agents.
Implement Concurrency
Build working parallel agent systems using asyncio, Promise.all, and Claude Code's built-in parallelism.
Choose the Right Pattern
Select between map-reduce, scatter-gather, pipeline, and worker pool patterns based on your use case.
Handle Failures Gracefully
Build resilient systems that handle partial failures without losing work from successful agents.