Context Kubernetes: Declarative Orchestration of Enterprise Knowledge for Agentic AI Systems

Original Abstract

We introduce Context Kubernetes, an architecture for orchestrating enterprise knowledge in agentic AI systems, with a prototype implementation and eight experiments. The core observation is that delivering the right knowledge, to the right agent, with the right permissions, at the right freshness -- across an entire organization -- is structurally analogous to the container orchestration problem Kubernetes solved a decade ago. We formalize six core abstractions, a YAML-based declarative manifest for knowledge-architecture-as-code, a reconciliation loop, and a three-tier agent permission model where agent authority is always a strict subset of human authority. Three value experiments show: (1) without governance, agents serve phantom content from deleted sources and leak cross-domain data in 26.5% of queries; (2) without freshness monitoring, stale content is served silently -- with reconciliation, staleness is detected in under 1ms; (3) in five attack scenarios, flat permissions block 0/5 attacks, basic RBAC blocks 4/5, and the three-tier model blocks 5/5. Five correctness experiments confirm zero unauthorized deliveries, zero invariant violations, and architectural enforcement of out-of-band approval isolation that no surveyed enterprise platform provides. A survey of four major platforms (Microsoft, Salesforce, AWS, Google) documents that none architecturally isolates agent approval channels. We identify four properties that make context orchestration harder than container orchestration, and argue that these make the solution more valuable.