Parallelization

Let’s say that you’re compiling a huge codebase on a single-threaded CPU, every file patiently waiting its turn, one after another. Slow, right? But toss that workload onto a multi-core processor, and suddenly, you’re compiling modules simultaneously, blasting through tasks at lightning speed. That’s exactly the magic behind the parallelization pattern in agentic systems: multiple LLM tasks run independently and merge results to tackle big problems faster.

Parallelization comes in two forms:

• Dividing your giant compile task into isolated modules, each processed in parallel.

• Run the same computation multiple times concurrently and combine diverse outcomes for a richer, more reliable result.

In this lesson, we’ll explore the dividing pattern in depth. Together, we’ll go through a complete walkthrough, breaking a task into independent subtasks, assigning them to separate LLM processes, and seamlessly stitching their outputs back together. Let’s fire up those cores and turbocharge our agentic workflows.

When to use parallelization?

Parallelization shines brightest when your tasks naturally split into independent chunks, or when you crave multiple viewpoints to reach higher-confidence outcomes. Think of parallelization as getting advice from multiple friends simultaneously: if each friend focuses solely on their area of expertise, you get sharper, faster insights.

For example, when dividing tasks into isolated modules, imagine you’re building guardrails for user interactions. One model handles your user queries while another independently screens for problematic content. This clear division of labor often outperforms having a single LLM juggle both tasks at once. Similarly, automated evaluations are smoother when each LLM assesses a distinct aspect of model performance, ensuring dedicated attention to every detail.

On the other hand, running the same computation multiple times concurrently excels in scenarios where multiple opinions matter. Suppose you’re analyzing code for vulnerabilities, asking different prompts to carry out a robust security check, reviewing and flagging potential risks results. Or, you’re evaluating content appropriateness through multiple independent evaluations to have a balanced judgment, reducing both false positives and negatives.

The bottom line is that when tasks can benefit from independent analysis or diverse viewpoints, a parallelization pattern will turbocharge your agentic workflows, making them faster, more reliable, and sharper.

How to build an agentic system that uses parallelization?

Imagine an AI agent acting as the front door for a customer support system. Before it can process a user’s message, it must answer two critical questions, and it needs to do it fast: 

• Is this a legitimate support query?

• Is it safe from security threats?

Solving this challenge efficiently is a perfect use case for the parallelization pattern. In this lesson, we’ll build an agent that performs both ...