A single model answering a single question needs no orchestration. A system that has to retrieve a document, call a specialised model to summarise it, check the result against a rule engine, and route the outcome to the right person needs something coordinating all of that: deciding the sequence, handling a step’s failure, and passing the right output from one stage into the next stage’s input.
That coordination layer is the orchestration. It’s what turns a collection of individually capable AI components into a single working system, and it’s usually where the real engineering effort in a production AI deployment goes, not into the models themselves, which are mostly bought rather than built.
It’s also where failures tend to concentrate. A single model failing produces one wrong answer; a poorly orchestrated multi-step system can compound a small error at step one into a much larger one by step five, which is why logging and observability at each handoff point matter as much as the logic connecting them.