The Rise of Parallel Programming: Why Developers Are Running Multiple AI Agents at Once
A quiet revolution is happening in how developers work. Instead of focusing on one task at a time—the traditional “flow state” approach—engineers are increasingly kicking off multiple AI agents simultaneously, managing several workstreams in parallel.
Is this the future of 10x productivity, or a recipe for chaos?
The Core Insight
The trend started with power users like Simon Willison, who initially shared the skepticism many developers feel: “AI-generated code needs to be reviewed, which means the natural bottleneck is how fast I can review the results.”
But something shifted. As Willison describes it: “Over the past few weeks I’ve noticed myself quietly starting to embrace the parallel coding agent lifestyle.”
The key insight? Not all tasks require your full attention. Research, maintenance work, and well-defined directed tasks can run in the background while you focus on reviewing and landing your primary work.
Anthropic engineer Sid Bidasaria mentions casually running “a few agents” throughout conversations. Senior engineers everywhere are discovering that skills they developed for leading teams—context switching, code reviewing across workstreams, handling interruptions, directing colleagues—translate directly to managing agent swarms.
Why This Matters
Flow state is being redefined. For decades, deep focus was the gold standard. Interruptions were the enemy. Now, the most productive engineers might be those who thrive on juggling—the same senior developers who already context-switch constantly as tech leads.
Junior vs. senior dynamics shift. The qualities that make a good tech lead—keeping parallel workflows in mind, reviewing code across multiple workstreams, effective written communication—suddenly become relevant for any developer who wants to multiply their output.
Sustainability is uncertain. Flask creator Armin Ronacher tried parallel agents but scaled back: “It’s only so much my mind can review!” Not everyone finds this workflow sustainable.
Key Takeaways
The engineers finding success with parallel agents follow certain patterns:
- Testing is non-negotiable. All projects have unit tests because you can’t trust unvalidated work
- Tasks stay small and descriptive. Clear scope, clear examples, clear success criteria
- Regular refactoring passes. Every third or fourth task asks the agent to clean up its own code
- Manual involvement stays constant. Keep your IDE open, make small changes by hand, stay aware of the codebase
- Process discipline matters more. “Mandating” engineering practices leads to better AI results
Looking Ahead
We’re in experimental territory. Maybe parallel agents will reveal that focused work produces more reliable software over time. Maybe engineers juggling multiple agents will ship faster but accumulate technical debt.
What seems certain: more developers will try this approach. The ones who succeed will likely be those who already excel at the messy, interrupt-driven reality of senior engineering work.
Software engineering basics—testing, small tasks, code review, refactoring—matter more than ever when you’re not writing the code yourself.
Based on analysis of “New trend: programming by kicking off parallel AI agents” by Gergely Orosz