What Is Agent Orchestration
Agent orchestration is the control plane for multi-agent systems. When you have more than one AI agent, something must decide which agent runs first, what each agent receives as input, how outputs combine, and when the overall workflow is complete. That decision layer is orchestration.
Think of it as a conductor for an orchestra. Individual agents are musicians with specific skills. The conductor does not play an instrument. The conductor assigns parts, cues entrances, and ensures the performance stays coherent. Orchestration does the same for AI agents: it does not execute tasks itself, but it coordinates who does what and when.
In practice, orchestration handles task routing, state management, error recovery, and result aggregation. It sits between the user or system that initiates a goal and the agents that execute subtasks. This separation is what makes multi-agent workflows manageable. Without orchestration, you have a collection of agents with no clear coordination. With it, you have a system.
How Agent Orchestration Works
Orchestration typically follows a loop: receive goal, decompose into subtasks, assign to agents, collect results, check completion, repeat or finish. The orchestrator maintains workflow state and passes context between agents. It does not need to understand domain details; it needs to understand dependencies and sequencing.
Three core responsibilities define orchestration. First, routing: given a subtask, which agent should handle it? Routing can be rule-based (support tickets go to the support agent) or model-based (the orchestrator reasons over task type and agent capabilities). Second, state management: what has been done, what is in progress, and what remains? State enables idempotency, retries, and audit trails. Third, failure handling: when an agent fails or times out, the orchestrator decides whether to retry, route elsewhere, or escalate.
Many implementations use a supervisor pattern. A supervisor agent receives the high-level goal, produces a plan with subtasks, and delegates each subtask to a specialist agent. The specialist returns a result; the supervisor evaluates it and either continues the plan or adjusts. This pattern scales well when subtasks are relatively independent and specialists are well-defined. For more linear workflows, a pipeline pattern works: Agent A runs, passes output to Agent B, which passes to Agent C. The orchestrator enforces the pipeline order and handles handoffs.
Use Cases for Agent Orchestration
Orchestration shines in scenarios where a single agent cannot complete the full workflow. Customer support escalation is a classic example: an intake agent triages the ticket, a research agent gathers account context, a drafting agent writes the response, and a quality agent reviews before sending. The orchestrator ensures the right agent runs at each stage and that outputs flow correctly.
Content production pipelines benefit from orchestration. A planning agent outlines the structure, a writing agent drafts sections, an editing agent refines tone, and a fact-check agent validates claims. Each agent has a narrow scope; orchestration ensures the pipeline completes in order and handles revisions. Research workflows similarly benefit: a search agent finds sources, a synthesis agent summarizes, and a citation agent formats references. The orchestrator coordinates these steps and aggregates the final output.
Enterprise automation often combines multiple domains. An order-processing workflow might involve an inventory agent, a pricing agent, a fraud-check agent, and a fulfillment agent. Orchestration ensures each runs in sequence or parallel as needed, with proper handoffs and error handling. The same pattern applies to AI agents for business processes across sales, support, and operations.
Limitations and Safety
Orchestration adds complexity. More agents and more handoffs mean more failure modes. State can become inconsistent if the orchestrator crashes mid-workflow. Retries can cause duplicate actions if not designed carefully. Teams should start with simple orchestration and add sophistication only when a single agent is insufficient.
Security considerations matter. The orchestrator often has broad visibility into workflow state and agent outputs. Access control should limit who can inspect or modify orchestration configuration. Agent-to-agent communication should be validated to prevent injection or privilege escalation. For sensitive workflows, orchestration logs should be auditable and tamper-resistant.
Orchestration does not eliminate the need for agent monitoring. It centralizes coordination but each agent still needs guardrails, validation, and observability. The orchestrator should enforce policy at the workflow level (e.g., no agent may run more than N times per goal) while individual agents enforce domain-level constraints.
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