Beyond Bigger Windows: Why Architecture Matters for Enterprise AI

Enterprise AI failures stem primarily from context management challenges in multi-agent systems rather than model capability limitations. This research proposes adopting computer architecture principles—particularly memory hierarchies and cache coherence protocols—to manage how agents share and access information at scale. Implementing these architectural solutions could prevent the performance degradation that currently occurs when token accumulation overwhelms context windows in complex agent workflows.