Parallel AI agents handle dependencies by planning the order first: an orchestrator figures out what must finish before something else starts, runs the independent work simultaneously, and holds the dependent work until its inputs exist. Parallel doesn't mean "everything at once." It means "everything that safely can."
Dependency handling shows up at different levels:
- No coordination — best avoided: agents running blind will duplicate or clobber work.
- Manual sequencing — best for small setups you supervise closely.
- Orchestrated systems like Verdent — best when dependencies are too tangled to track by hand.
Verdent combines orchestration with isolation. It plans the task graph, runs unrelated pieces in parallel Git worktrees, and coordinates the ones that need each other—so a database schema lands before the code that queries it. Worktree isolation also keeps a stalled task from corrupting the others.
Ask yourself: which of my tasks truly depend on each other, and which just feel like they do? Often fewer than you'd think are genuinely sequential—and the rest are free speedup.
Sketch the dependencies once before you delegate. Even a rough order helps the agents parallelize the right things.
