𝗬𝗼𝘂𝗿 𝗔𝗜 𝗳𝗲𝗲𝗹𝘀 𝘀𝗹𝗼𝘄? 𝗠𝗮𝘆𝗯𝗲 𝗶𝘁'𝘀 𝗻𝗼𝘁 𝗱𝘂𝗺𝗯.
AI often feels slow because it works on one task at a time. It sits in a queue. You wait for one module to finish before the next starts.
You can fix this by using parallel agents.
Many modules do not depend on each other. If you split them up, several agents can work at the same time. You get the same work done in much less time.
Here is how to build a parallel AI workflow:
Fix your architecture first Parallelism requires a clean design. Your modules must be loosely coupled. They should communicate through clear interfaces. If you force parallelism on messy code, you create a knot of errors. Use AI during the design phase to plan these interfaces.
Assign specific roles Use different models for different tasks to save time and money:
- The Lead (Opus): Manages the big picture, assigns work, and performs final checks.
- The Planner (Sonnet): Handles test-driven development (TDD) and implementation plans.
- The Worker (Haiku): Writes the actual code and runs tests.
Set your limits More agents are not always better. I tried setting 10 agents at once, but my machine ran out of memory. I moved down to 5 agents. This gave me a 10x speedup when combined with other tasks. Match your concurrency to your machine and your quota.
Use the Lead for reviews Do not hire a separate agent to review the work. That agent has to learn the whole task from scratch, which wastes tokens. Let the Lead agent do the review. It already knows what it assigned, so it can check the work faster and more accurately.
Avoid unnecessary splitting Some tasks must stay in order. If you force a split on tightly coupled modules, quality drops. Tell your AI: "These modules are coupled, do not force a split."
How to start today:
- Add "Parallelize when you can" to your global instructions.
- Increase your max concurrent subagents in your settings.
- Remind the AI to parallelize every time you give a task.
Optional learning community: https://t.me/GyaanSetuAi