agent-orchestrator

To select the right AI agent before spawning subagents, you provide task context to the orchestrator, which evaluates signals like research-first needs or debugging pivots to determine the optimal agent. This prevents misallocation and maximizes task outcomes.

Agent orchestration is the process of intelligently selecting and dispatching tasks to suitable subagents before execution. It improves workflow automation by using an agent selection matrix to evaluate signals, reducing misallocation and streamlining complex coordinated workflows.

Yes, you can use a structured dispatch workflow for debugging sessions and parallel processing. The orchestrator evaluates auto-detection signals to guide task dispatch, supporting diverse scenarios including safety checks, autonomous loops, and implementation.

The best way to ensure deterministic agent selection is to apply SLICE-aligned governance and auto-detection signals before spawning subagents. This structured dispatch workflow guides which agent should act in each scenario, ensuring compliance and quality.

You should not use an AI agent orchestrator when a task requires no subagent coordination or falls outside software development contexts. If your workflow lacks multiple agent types or complex chaining patterns, direct implementation without orchestration is more efficient.