Blog Category: Context Management

A context catalog makes metadata usable by AI agents and humans. Learn how it differs from a data catalog.

Context-aware AI agents need more than clever prompts. See why context-awareness is an infrastructure problem, and what production agents actually require.

Why the context layer in financial services starts at the glossary, not the retrieval engine, and what it takes to build one regulators trust.

Everyone’s building a context layer for AI. Most are building the wrong one. Here’s what enterprises actually need.

Context layer vs semantic layer: What each does, how they relate, and why both need context management infrastructure.

RAG is a retrieval pattern. Context management is the infrastructure that makes it work at scale. Learn the difference.

Context engineers build the systems that make AI agents reliable. Here’s what the role involves and why data engineers are a natural fit.

Context engineering vs prompt engineering: What changed, what’s different, and the infrastructure layer most teams are missing.

Context engineering optimizes one agent. Context management scales trusted context across all of them. See how.

Context window optimization techniques for AI agents, plus why upstream context quality determines the ceiling.

AI can write SQL. Context management is the new skill set that keeps data analysts indispensable.