Agent Orchestration Patterns

Coordinating multiple AI agents involves defining an orchestrator that manages agent roles, communication protocols, and handoffs. Use patterns like sequential pipelines where each agent's output feeds the next, parallel fan-out for independent tasks, or specialist consultation where primary agents consult domain experts. This ensures clear responsibility and structured collaboration to solve complex problems.

Common orchestration patterns include sequential pipeline (ordered steps with data flow between agents), parallel fan-out (independent tasks running concurrently then merged), specialist consultation (primary agent consulting domain specialists when needed), and debate and synthesis (multiple agents proposing solutions then consolidating results). Choose based on task dependencies and complexity.

Assign each agent a single responsibility to avoid confusion and define explicit communication protocols specifying how agents share data and hand off work. Use completion signals to mark task boundaries, structure requests clearly, and designate an orchestrator to coordinate execution. This prevents conflicts and ensures seamless collaboration.

Yes. Agent orchestration is designed for scenarios with specialized roles—research agents gather information, analysis agents process findings, and writing agents produce output. An orchestrator manages the workflow, defines role boundaries, and ensures each agent receives proper input and timing, making it ideal for multi-phase knowledge work.

Sequential pipeline executes agents in order, with each agent waiting for the previous one to complete and passing output as input. Parallel fan-out runs independent agent tasks simultaneously, then fuses results together. Choose sequential for dependent tasks requiring step-by-step refinement and parallel when tasks are independent to improve speed.

Use specialist consultation when a primary agent handles most decisions but occasionally needs expert input on niche topics. This pattern is more efficient than running all agents sequentially and reduces unnecessary processing by only engaging specialists on demand, ideal for workflows with clear primary responsibility.