Agentic Systems and Their Design Patterns — Part 3: Parallelization

2026-04-23 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

This article, Part 3 of a series on agentic systems, details the "Parallelization" design pattern, which executes multiple independent subtasks simultaneously to reduce latency and enhance output breadth or confidence. It distinguishes between "Fan-out / Fan-in," where a single task is split into different subtasks for parallel execution and then merged, and "Voting / Ensemble," where the same task is sent to multiple agents for redundant processing and aggregated results. The pattern is suitable when subtasks are independent, latency is critical, broad perspectives are needed, or higher confidence is required. The article outlines a three-stage flow: decomposition, parallel execution, and merging, emphasizing the critical role of a robust merge step. A concrete example demonstrates building a competitive intelligence report using CrewAI, where specialist agents work in parallel before a synthesizer agent combines their outputs.

Key takeaway

For AI Engineers designing agentic systems, implementing parallelization can significantly reduce task latency and improve report breadth or confidence. You should identify independent subtasks and leverage frameworks like CrewAI's `Process.hierarchical` to run them concurrently. Pay particular attention to the synthesis stage by providing a clear output schema and constraining individual agent outputs to ensure a coherent, high-quality final result, avoiding common pitfalls like incoherent merges or context window overflow.

Key insights

Parallelization in agentic systems executes independent subtasks concurrently to improve speed and output quality.

Principles

• Parallelize genuinely independent subtasks.
• Latency reduction is a key benefit.
• The merge step determines output quality.

Method

Decompose input into independent subtasks, dispatch to parallel agents, then collect and synthesize outputs using a dedicated agent with a defined schema and constrained input lengths.

In practice

• Use `Process.hierarchical` in CrewAI for parallel execution.
• Define a clear schema for the synthesis agent.
• Constrain parallel agent output lengths (e.g., 200-300 words).

Topics

• Agentic Systems
• Parallelization Pattern
• Fan-out / Fan-in
• Voting / Ensemble
• CrewAI Framework

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.