10 Principles of Enterprise Context Platforms

A framework for evaluating whether your context platform is built for production-grade agentic AI 

95% of AI projects fail to reach production, and Gartner predicts nearly half of agentic AI projects will be canceled by 2027. These aren’t model failures. They’re context failures: agents operating without accurate, governed, current information about the data they’re asked to reason over.

This guide lays out ten principles for evaluating whether a context platform can actually serve AI agents at production scale, from how broadly it ingests structured, unstructured, and semantic sources, to whether it activates context through more than a bare MCP connection, to whether it can reconstruct exactly what context an agent acted on. Along the way, it gives you a clear model for where context platforms fit in the broader AI reference architecture, and what that means for how you design your own stack.

What’s inside:

• Why 91% of organizations plan to build or buy a context platform in the next 12 months, and what separates context management from context engineering
• The 10 principles spanning ingestion breadth, semantic source-of-truth, expert validation, drift resolution, universal shareability, agentic-scale architecture, and auditability
• Why event-driven architecture, not batch refresh, is the baseline requirement for serving agents that query context continuously and at machine speed
• Why a timestamped, versioned context graph is the capability regulated buyers should weigh most heavily