Don't Go Monolithic; the Enterprise Agent Stack Is Stratifying (philippdubach.com)

Recent insights from Arvind Jain, CEO of Glean, highlight a significant shift in the enterprise AI landscape, emphasizing the stratification of the AI agent stack into six layers: security, context, models, orchestration, agents, and interfaces. As organizations increasingly adopt diverse AI models—now averaging over five per enterprise—the focus moves beyond mere model selection to the contextual layer that serves as a critical differentiator. Jain argues that while traditional AI efforts concentrate on connecting data and enhancing retrieval systems, the true value lies in understanding organizational processes, encapsulated by depth in context, which records not just outcomes but the intricate mechanisms that lead to those outcomes. This shift carries profound implications for the AI/ML community, as enterprises must prioritize developing and maintaining their unique organizational world model, akin to proprietary knowledge. The anticipated rise in enterprise AI applications, projected to reach 40% adoption by 2026, may be undermined by the challenge of embedding deep contextual knowledge into AI systems. With a warning from Gartner that over 40% of such projects could fail due to insufficient context, the demand for multi-layer interoperability and a strategy that separates context from standardized models becomes paramount. Organizations that proactively build and protect their contextual insights are likely to gain a competitive advantage, ensuring their AI agents operate effectively in alignment with real-world dynamics rather than producing generic outputs.