For the last year, the standard playbook was simple: connect your enterprise data to a massive, centralized API and let the model do the work. It worked beautifully for chatbots.
But we are no longer building chatbots. We are building agentic systems.
When an agent operates, it doesn't just answer a prompt. It reasons, queries a database, reflects on the result, realizes it made a mistake, and loops back to try again. A single user request might trigger 50 background inferences.
The economics of these "Agentic Loops" are brutal. When you pay per token, they simply destroy enterprise budgets at scale. Inference costs now consume up to 85% of total enterprise AI budgets, dwarfing the initial CapEx costs of model training.
This realization is driving a massive architectural shift.
To make agentic workflows economically viable, enterprises and governments are moving to "Sovereign AI" architectures. They are taking powerful open-weights models, shrinking them to specific use cases, and deploying them on their own internal infrastructure.
The strategic question for decision-makers is no longer "which model should we use?"
It is "do we have the infrastructure to sustain agentic workflows at scale?"
The true capability now lies in orchestration: how efficiently you can route tasks to small, cheap, sovereign models running securely inside your own perimeter.