The enterprise software stack of the 1990s and 2000s was characterized by a layer of middleware that nobody wanted to build but everybody needed. Middleware sat between applications and infrastructure, handling routing, protocol translation, state management, and integration glue. IBM WebSphere, TIBCO, MuleSoft — these were not glamorous products. They were load-bearing parts of systems that could not function without them.
AI agent orchestration is occupying the same position in the emerging AI application stack. Nobody who is building the interesting application-layer things wants to be in the business of managing model routing, context assembly, retry logic, and failure recovery. But without this layer, agent systems cannot be deployed at scale. The orchestration layer is where the unglamorous but essential work happens.
What Orchestration Actually Does
Agent orchestration is not just chaining LLM calls together. Done well, it handles several distinct concerns. Context management: deciding what information needs to be present in each call, how to assemble it efficiently, and what to drop when the window is constrained. State management: tracking where an agent is in a multi-step task, persisting intermediate results, handling the case where a step fails. Routing: deciding which model to use for which subtask, balancing capability against cost and latency. Retry and fallback: handling model failures, rate limits, and unexpected outputs in a way that degrades gracefully.
These concerns are present in every non-trivial agent deployment. Teams either build them into their application logic — making it hard to maintain and impossible to reuse — or they reach for an orchestration layer. The pattern repeats what happened with middleware: initially everyone builds it themselves, then a handful of platforms emerge, and eventually the ecosystem converges on the two or three that got the abstraction right.
The Architecture Question
The one thing I am less sure about than I was a year ago: whether the right architecture for orchestration is a runtime (a system that your code calls into) or a framework (a set of conventions your code is organized around). The runtime model offers more operational control; the framework model offers more developer flexibility. Both have real proponents and real production deployments. The answer may ultimately depend on the use case, with different architecture patterns winning in different segments of the market.