Parallel AI subagents save time on reads but can hurt cost and quality on shared-state writes

A DEV Community article argues that spawning multiple AI subagents trades speed against cost and quality, much like the classic project triangle. Each parallel agent carries its own context and tokens, so running five on the same repository can multiply input costs without a proportional gain. Agents working in parallel also cannot see each other's progress, which makes their combined output prone to conflicts when they edit related files. The author recommends fanning out only for read-only tasks such as searching, review, and summarising, while keeping build and commit work in a single agent. The piece suggests recording this rule in a team's CLAUDE.md file so everyone follows the same approach.