Addressing the critical gap between information retrieval and model execution, Immanuel Gabriel introduces a framework focused on "context integrity" to prevent AI agents from acting on stale, unattributable, or contradictory data. While current industry efforts prioritize prompt tuning and retrieval ranking, this approach argues that similarity scores fail to account for the actual utility of context. The proposed system evaluates context based on three specific properties: freshness, which utilizes a decay-adjusted model tailored to the source type; attributability, ensuring every piece of data can be traced to a specific source; and coherence, which detects internal contradictions within the retrieved chunks. To move beyond mere promises, the architecture implements a trust layer where every evaluation is recorded in a ledger and signed using an Ed25519 key. This enables independent, offline verification of verdicts without requiring API access or server callbacks. Currently, the implementation includes an evaluation engine, an interface for the Model Context Protocol, and a hosted endpoint for demonstrations. Future development aims to include a safety harness with pass, warn, refresh, and block capabilities, alongside a telemetry-driven control-plane dashboard to provide verifiable proof of context integrity in high-stakes or regulated workflows.