Tenure argues that AI governance cannot work as a tool agents can choose to call—it must be enforced as an invisible layer beneath every request, like network policy in infrastructure.

What happened: Tenure, a research effort, identifies a fundamental flaw in how most AI agents are built today. Governance, memory, and policy are currently positioned as optional tools the model can invoke, but this makes them dependent on the model's cooperation. Tenure proposes instead an 'AI context control plane'—infrastructure that sits in the path of every request and enforces governance, memory management, and audit logging before the model ever sees the request.

Why it matters: As AI moves from personal experiment to real enterprise work, fragmented context across different tools (coding agents, chat clients, ticketing systems, notebooks) creates governance problems. A team decision made in one tool disappears when a developer switches clients; a policy correction requires manual copying into every agent. Without a shared, governed substrate for context beneath all tools, enterprises cannot reliably ensure that access controls, audit trails, and coding conventions actually apply—they become suggestions the model may or may not follow.

What to watch: The structural precedent already exists in infrastructure; containers do not ask permission before sending packets because network policy is enforced at a layer they cannot opt out of. Tenure is positioning itself to bring that same enforcement model to enterprise AI, starting with governed memory and state beneath model requests—moving governance from optional tool-calling to mandatory request-path enforcement.