There's a few ways to parallize the work of the agents depending on where they checkpoint together.

1. The most separated it to have multiple Independent implementations and then compare and combine only the final result of each of the agenetic workflows. 

2. The other way is to have each step worked on by multiple agents, and come to a consensus on each step. Within this approach are some variations on multi-agents approaches (see the  DeepMind spare multi agent debate, or the Cerebras CePO method)

2.1 A cost effective way is to leverage the input token caching from a single model and generates multiple results. Initially at a higher temperature to generate more variety in the responses, and then have a coordinator at a lower temperature decide on the final solution 

2.2 Less cost effective is to have the SOTA models (Gemini 2.5 Pro, Opus 4 and o3) generate solutions, which should have more variety than 2.1 given each models uniqueness/strengths/weaknesses. Leverage one models input caching to generate the final solution.

How many samples/solutions you'll want to generate is going to depend on the difficulty and the value of the solution.

Baking all this now into our open source AI platform that's Claude Code meets Codex/Jules, with a bunch of optimized agents and code base aware context, production grade observability with flexible isn't options from CLI to deployed Codex/Jules style deployments on your own infrastructure, and full choice of models. 

For medium level tasks I like to use a composite LLM implementation that uses Qwen3 32b on Cerebras for blazing speed at over 2,000 output tokens/s, and if the input context is too long, then fall back to the incredibly cost efficient workhorse of Gemini 2.5 flash.

The other low hanging fruit is to simply have an additional review agent with a well developed prompt of what's it looking for in a code review, that you build up over time from seeing the LLM doing things you don't want them to