Embracing the parallel coding agent lifestyle
For a while now I’ve been hearing from engineers who run multiple coding agents at once—firing up several Claude Code or Codex CLI instances at the same time, sometimes in the same repo, sometimes against multiple checkouts or git worktrees.
I was pretty skeptical about this at first. AI-generated code needs to be reviewed, which means the natural bottleneck on all of this is how fast I can review the results. It’s tough keeping up with just a single LLM given how fast they can churn things out, where’s the benefit from running more than one at a time if it just leaves me further behind?
Despite my misgivings, over the past few weeks I’ve noticed myself quietly starting to embrace the parallel coding agent lifestyle.
I can only focus on reviewing and landing one significant change at a time, but I’m finding an increasing number of tasks that can still be fired off in parallel without adding too much cognitive overhead to my primary work.
Here are some patterns I’ve found for applying parallel agents effectively.
Research for proof of concepts
The first category of tasks I’ve been applying this pattern to is research.
Research tasks answer questions or provide recommendations without making modifications to a project that you plan to keep.
A lot of software projects start with a proof of concept. Can Yjs be used to implement a simple collaborative note writing tool with a Python backend? The libraries exist, but do they work when you wire them together?
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