When AI agents fail on enterprise data, teams tend to blame the model. In practice, the root cause is usually upstream: a schema changed silently, a transformation behaved differently between development and production, or a data source was swapped without updating downstream assumptions. The data layer is the unreliable component, not the large language model (LLM).In this session, Tom Kaltofen examines why coding agents, such as Cursor and Claude Code, handle context reliably while enterprise data agents struggle. Coding agents operate on deterministic context: files have fixed paths, functions have type signatures, and tests provide ground truth. Enterprise data has none of that by default. From there, he introduces the handover layer pattern: data producers publish transformations with explicit input/output contracts, consumers declare what they need, and a runtime resolves how to compute it. The architecture follows the same principles as Kubernetes: declarative specs, independent controllers, and runtime reconciliation.A live demonstration shows an AI agent discovering transformations through a plugin registry, applying PII redaction, and switching between data sources without hard-coded paths. The demo runs on mloda, an open-source Python framework.The session covers what worked, what did not, and where the pattern breaks down. Attendees leave with a concrete architectural pattern for separating “what” to compute from “how” to compute it, and a clearer picture of why existing tooling, such as feature stores, semantic layers, and data catalogs, was not designed for AI agent consumers.