The architecture that decides when and how to call an LLM, what context to give it, and what to do with the response.
LLM orchestration is the layer of code that coordinates calls to language models within a larger system. It handles when to invoke the model, what context to assemble, how to validate the output, when to retry or fall back, how to combine model calls with deterministic logic, and how to keep the resulting workflow observable. Orchestration is what turns a model API into a production AI feature.
Orchestration is where most production AI cost is wasted and most reliability is lost. Agent loops without iteration caps cause runaway cost spikes. Models asked to orchestrate workflows they should not own cause silent quality drift. Across the AI deployments Skopa has audited, orchestration cleanup typically delivers the single largest cost reduction — about 30% of total savings.
The model should be the smallest possible component of your workflow, not the orchestrator of it. Healthy orchestration separates planning from execution, moves deterministic logic out of the model entirely, sets hard caps on iterations and tool calls per turn, validates outputs structurally before acting on them, and traces every span end-to-end. Frameworks like LangGraph and CrewAI help, but the real win is the architectural discipline of deciding which steps genuinely need probabilistic reasoning and which do not.