Orchestration

Modern software rarely consists of a single program. Usually, many small programs work together, each handling only one task. One searches a database, another checks a customer number, a third writes a text. For a meaningful result to emerge, someone has to set the order and pass along the intermediate results. This very control is called orchestration. The image behind it is a conductor: he doesn’t play an instrument himself, but without him the musicians would play in disarray.

The conductor as a product in its own right

In the past, this control was a byproduct, hidden somewhere in the program code. Today it is often the actual core of a system. The reason is the number of participants involved: a single service on the internet can consist of dozens of individual parts running on different machines. Without clear control, this quickly becomes unmanageable.

With AI applications, there’s an additional cost argument. Large language models, i.e. programs that generate text, cost money per request. Good control routes simple questions to a small, cheap model and only difficult ones to a large one. With millions of requests per day, this determines the profitability of a product. That’s why companies selling pure orchestration software are now showing up in financial news.

A second point is reliability. Individual services fail, network connections break, responses arrive too late. The control layer then decides whether to retry, wait, or deliver a fallback solution. How stable a system appears often depends less on the individual parts than on this layer.

From fixed workflow to self-deciding controller

In the simplest case, orchestration is a fixed workflow. Developers write it down: first step A, then B, and if B fails, C instead. A program, the orchestrator, executes this plan. It calls the individual services, collects their responses, and passes them on to the next step. It also keeps track of where in the process things currently stand, so that after a disruption not everything has to start over from the beginning.

In AI systems, the workflow is often not known in advance. A typical chain looks like this: first the user’s question is checked, then matching documents are retrieved from a company archive, then a language model formulates an answer from them, and finally a validation rule checks whether the answer is permissible. Each of these steps is its own service. Orchestration connects them and logs what happened.

Systems in which a language model itself decides the order go a step further. These are called agents. They choose from a list of tools, such as a search engine or a calculator, and call them one after another. Here, orchestration is no longer the plan itself but the framework: it provides tools, limits the number of attempts, and stops if the model gets stuck in a loop. A common misconception is to equate orchestration with automation. Automation means that a single step runs without a human. Orchestration means that many such steps mesh together meaningfully.

Where the control layer becomes visible

The term is best known from data center operations. The software Kubernetes orchestrates containers, i.e. self-contained software packages, and automatically distributes them across available machines. If a machine fails, it restarts the affected packages elsewhere. When cloud providers mention orchestration in their quarterly reports, this is usually the layer they mean.

In everyday life, you encounter the principle without seeing it. A voice assistant on a phone first recognizes the words, then looks for the answer, checks the calendar or weather service, and speaks the result aloud. Even a car insurance company that processes a claim semi-automatically depends on such a chain. The control usually only becomes visible in the event of a failure, when a response takes unusually long or breaks off midway.

In product names, the word often appears as orchestration layer. Companies use it to sell the promise of not being tied to a single AI model. Whoever controls the control layer can swap out the underlying model as soon as a cheaper or better one appears. That is precisely why this layer is currently considered strategically valuable among investors.