Agent Orchestration

Agent orchestration is the practice of designing systems where multiple AI agents collaborate to accomplish tasks that no single agent could handle well alone. An orchestrator acts as the central coordinator -- it decides which agent should handle which subtask, passes context between agents, manages execution order, handles failures, and assembles the final output.

The simplest form of orchestration is sequential chaining: Agent A produces output that becomes Agent B's input. More sophisticated patterns include parallel fan-out (multiple agents work on subtasks simultaneously), hierarchical delegation (a supervisor agent breaks down a goal and assigns pieces to specialist agents), and competitive routing (multiple agents attempt the same task and the best result is selected). Frameworks like LangGraph, CrewAI, and AutoGen provide abstractions for building these patterns.

Orchestration becomes necessary when tasks are too complex or too broad for a single prompt or a single model. A research task might need one agent to search the web, another to analyze documents, a third to synthesize findings, and a fourth to fact-check the synthesis. Each agent can have different tools, system prompts, and even different underlying models optimized for its specific role.

The hard problems in agent orchestration are error propagation, context management, and cost control. When Agent B fails because Agent A gave it bad input, the orchestrator needs to decide whether to retry, fall back, or escalate. As agents pass context to each other, information can degrade or bloat -- a phenomenon sometimes called context rot. And because each agent call costs tokens, poorly designed orchestration can burn through API budgets quickly without producing better results than a single well-prompted agent.