With agentic interfaces like Claude Code, Cursor, and other LLM-based tools going mainstream, developers increasingly run multiple agents simultaneously. Engineers like Sid Bidasaria and Simon Willison report fewer reservations and higher throughput after adopting this style. By Gergely Orosz.
Some points discussed in the article:
- A growing number of engineers are adopting parallel AI coding agents, running multiple agent instances simultaneously to accelerate work.
- Early adopters—mostly senior and staff-level engineers—find this method boosts productivity for research, maintenance, and decomposed tasks.
- Parallel agent workflows may challenge traditional notions of software engineering “flow” and single-threaded focus.
- New productivity bottlenecks shift from coding to reviewing and validating agent outputs.
- Effective use of parallel agents requires strong engineering fundamentals: testing, small tasks, refactoring cycles, and vigilant code review.
The article stresses rigorous engineering discipline: comprehensive tests, narrow task definitions, periodic refactoring, active review, and developers still handling small edits themselves. These practices mitigate AI unpredictability and improve outcomes. Good read!
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