---
title: "Workflow: Parallelization"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Workflow: Parallelization

LLMs can sometimes work simultaneously on a task and have their outputs aggregated programmatically. This parallelization workflow comes in two key variations:

- **Sectioning:** Breaking a task into independent subtasks that run in parallel.
- **Voting:** Running the same task multiple times to get diverse outputs.

(Workflow: Parallelization by Antropic)[https://www.anthropic.com/research/building-effective-agents]

(Implement Parallelization Workflow using LangGraph)[https://langchain-ai.github.io/langgraph/tutorials/workflows/#prompt-chaining]

(Task Method Spec)[https://langchain-ai.github.io/langgraph/reference/func/?h=pregel#langgraph.func.task]

### How the Workflow Works

The core idea is simple: instead of relying on a single, monolithic LLM call for a complex task, you divide the work so that multiple LLM calls run concurrently, and then aggregate their outputs. This is achieved through two main strategies:

1. **Sectioning:**  
   - **Concept:** Break down a complex task into several independent subtasks that can be solved in parallel.  
   - **Example:** If you need to evaluate a business idea, you might create separate subtasks for market analysis, risk assessment, resource planning, etc. Each subtask is handled by an independent LLM call, and their results are later combined to form a holistic plan.

2. **Voting:**  
   - **Concept:** Run the same LLM prompt multiple times concurrently. The idea is to get diverse outputs (or “votes”) that can then be aggregated—through majority voting, averaging, or another consensus method—to increase the confidence and robustness of the final result.  
   - **Example:** When reviewing a piece of code for vulnerabilities, you could run several LLM evaluations in parallel. If the majority flag a particular vulnerability, you have higher confidence that it’s a real issue.

**Aggregation:**  
After the parallel calls complete (whether they are independent subtasks or repeated evaluations), a final aggregation step collects all the outputs. This aggregation might simply concatenate the results (in the case of sectioning) or apply a decision function (in the case of voting) to produce a final answer that is both faster and, ideally, more reliable.

## When to Use This Workflow

Parallelization is effective when:
- **Speed is critical:** Dividing a task into independent subtasks allows them to run simultaneously, reducing overall latency.
- **Multiple perspectives are needed:** For complex tasks with multiple considerations, having separate LLM calls handle each aspect can yield higher confidence results than a single monolithic response.

## Examples of Practical Use Cases

### Sectioning

- **Guardrails:**  
  One model instance processes user queries while another screens the queries for inappropriate content or requests. This separation tends to yield better performance than having the same LLM handle both core responses and guardrails.

- **Automated Evaluations (Evals):**  
  When evaluating LLM performance, each LLM call can focus on a different aspect of the model’s output, ensuring a more comprehensive evaluation.

### Voting

- **Code Vulnerability Review:**  
  Multiple prompts review a piece of code in parallel to identify vulnerabilities. Each LLM instance provides its assessment, and the aggregated output flags potential issues when a certain vote threshold is reached.

- **Content Moderation:**  
  When assessing whether a piece of content is inappropriate, parallel prompts can evaluate different facets of the content. Using a voting mechanism (or multiple thresholds) helps balance false positives and negatives, ensuring a more accurate moderation decision.

### Summary

The **Parallelization Workflow** for LLMs leverages two key strategies:

- **Sectioning:** Divide a complex task into independent subtasks, each handled by a separate LLM call.
- **Voting:** Run the same prompt multiple times in parallel to collect diverse outputs and aggregate them for improved reliability.

This approach is already being explored in frameworks like LangChain and in various agent-based systems. Researchers and developers have found that aggregating multiple LLM outputs—whether by splitting a task or by combining repeated evaluations—can both speed up the process and enhance output quality.

In practical terms, implementing this workflow involves:
- Structuring your application to dispatch multiple asynchronous LLM calls.
- Manage parallel execution.
- Aggregating the outputs via simple concatenation or more sophisticated voting/decision algorithms.

## Next Step
Run the project now and see the Parallelization Workflows in action
