Many Hands, One Job
One model call rarely finishes a real task. You need a structure: who does what, in what order, and what happens when a step fails. That structure is orchestration.
Three patterns
A workflow is a fixed path: step A then step B. Deterministic, easy to reason about, and often the right answer — most “agents” should be workflows. A router sends each request to a specialist. A supervisor (or multi-agent) pattern has a lead model that delegates to workers and checks their results. More autonomy buys flexibility and costs predictability; reach for it only when the path genuinely branches.
Durable execution
Agents are long-running, so they fail mid-task: a network error, a rate limit, a process restart. Durable execution treats an agent run as a resumable state machine. After every step, the state is saved — checkpointing — so a crash resumes from the last good step instead of starting over. This is ordinary distributed-systems engineering, and it is what separates a demo from something you can leave running.
Autonomy is a cost
Every extra agent step adds latency, cost, and failure modes. A two-step workflow with a good prompt beats a five-agent swarm with a vague one. Frameworks should make the simple case easy, not the elaborate case tempting.