Orchestration

Real AI applications are rarely a single call. They retrieve context, call a model, validate the result, branch on it, call tools, maybe call another model, and assemble an answer. Orchestration is the layer managing that: sequencing, state, retries, error handling and cost control.

The central design decision is how much control to hand the model. A workflow has steps you defined, with the model filling in specific slots — predictable, testable, debuggable. An agent decides its own steps — flexible, and much harder to reason about. Production systems overwhelmingly favor the workflow end, using agency only where the task genuinely cannot be enumerated in advance.

Orchestration frameworks provide the plumbing, though a great deal of production orchestration is ordinary application code.

Reliability in AI systems comes mostly from orchestration, not from model quality. Decomposing a hard task into checkable steps beats asking a better model to do the whole thing in one go, and it also makes failures diagnosable — you can see which step went wrong.

Default to the most constrained structure that solves the problem. If you can enumerate the steps, enumerate them; reach for agent autonomy only when you genuinely can't.