Parallel Workflows

Introduction

A parallel workflow runs independent steps simultaneously and joins their results. Cuts latency dramatically when steps don't depend on each other.

Beginner analogy: three chefs prepping three dishes at once instead of one after another.

Understanding the topic

Core concepts:

• Use Promise.all / asyncio.gather to parallelise.
• Aggregate results in a join step.
• Independent steps only — beware shared state.
• Watch rate limits — parallel = burst traffic.
• Best for: multi-source research, ensemble LLM calls.

Syntax reference

Visual workflow / architecture:

           ┌─► search Bing ─┐
goal ─► fan-out ─► search Google ┼─► join ─► summarise
           └─► search DuckDuckGo ┘

Real-world use

Perplexity-style search agents run 5+ queries in parallel; ensembling LLMs (Claude + GPT-4) on the same task in parallel and picking the best.

Best practices

• Use parallelism for I/O-bound steps.
• Respect provider rate limits.

Common mistakes

• Parallelising steps that share state without locks.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. When is parallelism a win?
• Q2. Three operational risks of parallelism.
• Q3. Scenario: parallel research returns conflicting facts. How do you resolve?