Prioritize tasks across multiple AI agents by ranking dependencies first, then user value, risk, feedback speed, and shared-resource constraints. Sending every high-priority item at once can block the critical path or create conflicting changes.
Build a queue with these fields:
- Dependency: What must finish before this task can start or merge?
- Value: Which accepted outcome matters most to users or delivery?
- Risk: Which decision needs early evidence before more work depends on it?
- Feedback time: Which task can validate an assumption quickly?
- Ownership: Which files, interfaces, or environments does the task control?
- Capacity: Does it compete for the same test environment, reviewer, or service quota?
Run uncertainty-reducing work early. A short architecture probe or failing-test reproduction can prevent several agents from implementing against a wrong assumption. Put independent, low-conflict tasks in parallel. Keep migrations, shared interfaces, and release steps serialized unless their contracts are already stable.
Use a visible board with task IDs and states such as ready, active, blocked, review, and complete. An orchestrator can propose queue changes, but it should explain why a task moved and which dependency changed. Recalculate priority when evidence arrives; do not let an old numeric score override current reality.
Judge the schedule by accepted throughput, not agent utilization. An idle agent is cheaper than duplicate work or a blocked integration. Protect the critical path and keep review capacity in the plan.
Related reading: How to orchestrate multiple AI coding agents and How parallel agents share a codebase safely.
