Home/Agentic AI/Workflow Patterns/Fan-Out Patterns

Workflow Design Patterns

Master proven patterns for designing scalable, maintainable agent workflows

Your Progress

0 / 5 completed
1
2
3
4
5

Fan-Out/Fan-In Patterns

The fan-out/fan-in pattern is one of the most powerful techniques for parallel processing. It splits work across multiple agents (fan-out), processes tasks concurrently, then aggregates results (fan-in).

Core Concepts

⫸

Fan-Out

Distribute work to multiple parallel workers

⚡

Process

Workers execute tasks independently and concurrently

⫷

Fan-In

Collect and merge results from all workers

Interactive: Fan-Out Simulator

Adjust the number of workers and run the simulation to see how fan-out improves throughput.

1 worker5 workers
Coordinator
⫸ Fan-Out
Worker 1
0 tasks
Worker 2
0 tasks
Worker 3
0 tasks
⫷ Fan-In
Results: 0/10
PERFORMANCE INSIGHT:
With 3 workers, tasks complete approximately 3x faster than sequential processing (assuming independent tasks).

Map-Reduce Pattern

A specialized fan-out/fan-in pattern popularized by big data systems:

Map Phase (Fan-Out)
Split data into chunks → Process each chunk independently → Emit key-value pairs
Shuffle Phase
Group all values by key → Send to appropriate reducers
Reduce Phase (Fan-In)
Aggregate values for each key → Produce final results

Use Cases & Examples

→
Web Scraping
Fan-out URLs to scrapers, fan-in collected data
→
Batch Processing
Distribute documents for analysis, merge summaries
→
A/B Testing
Run multiple strategies in parallel, compare results
→
Search Engines
Query multiple indexes concurrently, rank combined results

Implementation Considerations

✅ When to Use
  • • Tasks are independent (no dependencies)
  • • Work can be split into similar chunks
  • • Processing time is significant
  • • Results can be merged/aggregated
⚠️ Challenges
  • • Load balancing (uneven work distribution)
  • • Worker failures (need retries)
  • • Result ordering (if sequence matters)
  • • Overhead (coordination cost)

💡 Key Insight

Fan-out is about parallelism, not just distribution. Simply splitting work across agents doesn't help if they process sequentially. The power comes from concurrent execution—all workers running at the same time. Ensure your infrastructure (API rate limits, hardware, etc.) can actually support parallel processing before implementing fan-out patterns.

←Previous