Source: Bytebytego
As AI systems move from research prototypes into deployed applications, their bottlenecks have shifted from raw capability to operational constraints: real-time data integration, latency budgets, and permission systems that prevent agents from causing damage matter more than the middle-token problem. Companies building production agents are discovering that the limiting factor isn't whether a model can understand a 100K-token prompt, but whether it can reliably integrate live data, respond within SLA windows, and enforce hard boundaries on database access and action execution—problems that require infrastructure, not just better model weights.