A parallel AI agent coding workflow starts with a plan, splits the work into independent tasks, assigns those tasks to separate agents, and keeps each agent's changes isolated until review. The goal is coordinated concurrency, not uncontrolled parallel edits.
The benefit is speed, but only when the work is scoped and isolated. Parallel agents can handle frontend changes, backend updates, tests, documentation, and experiments at the same time. If they edit the same files without coordination, they create conflicts.
Verdent's parallel workflow combines planning, worker dispatch, workspace isolation, and review. Each worker can operate in a separate git worktree-based workspace, so changes stay separated until the user reviews them. The workflow ends with integration: inspect diffs, run checks, resolve conflicts, and merge only trusted work.
