A single AI agent can complete isolated tasks — write a function, summarize a document, generate a test. Orchestration addresses what happens when tasks depend on each other and different agents own different steps. The orchestrator decides which agent runs next, what context it receives, what constitutes successful completion, and what happens when something goes wrong.
Orchestration patterns range from simple sequential pipelines (A finishes, then B starts) to reactive event-driven systems (an event published by A triggers B automatically, without a central scheduler). Reactive patterns tend to be more resilient because they do not require a polling loop and naturally handle retries and partial failures.
Key orchestration concerns include: deadlock prevention when agents are waiting on each other, budget enforcement so no single agent exhausts shared token quotas, handling failures at any stage, and maintaining a tamper-evident audit trail of every decision each agent made.