Why AI Agent Context Should Be Treated as an Authority Control

Enterprise AI systems commonly maximize the data fed to agents, but a new technical perspective argues this approach is flawed and potentially risky. An agent's effective authority is shaped by both the information it can access and the actions it is permitted to take, meaning context control is as critical as capability control. A synthetic experiment called Token-Bleed R5 found that compact, governed context selection used up to 97.9% fewer prompt tokens than full-context stuffing while achieving higher accuracy. However, the same experiment revealed that governed selection still consumed nearly seven times more tokens than a simple lexical baseline, which itself scored zero on quality metrics, highlighting real cost trade-offs. The recommended architectural approach separates agent workflows into four planes — connection, context, capability, and evidence — treating the context layer as a formal authority control rather than a mere technical convenience.