Right now, the characterization of where an agent’s behavior is well-understood and where it is not lives in the heads of the engineers closest to the system. Externalizing this, making it auditable, and connecting it to deployment gates is a product with enterprise value. This parallels how Observability is the missing discipline for agent systems — you can't improve what you can't measure calls for telemetry as a first-class concern — trust boundary mapping is the verification equivalent, characterizing not just what the system is doing but where we can and cannot trust it.
This connects to Decision traces are the missing data layer — a trillion-dollar gap as the next step: once trust boundaries are mapped, behavioral records can reconstruct what an agent did, why, and whether it was operating within its characterized trust boundary at the time — linking verification to legal and financial infrastructure that does not exist yet. And Permissioned inference is harder than permissioned retrieval — enterprise context graphs need reasoning-level access control extends trust boundaries from agent behavior to data influence: not just where the agent is well-understood, but whose decision history it can draw reasoning from. When trust boundary mapping extends to learning from traces, Shadow execution enables safe trace learning — replay write operations without touching production data provides a concrete mechanism — replaying write operations in a shadow path so agents can learn from realistic flows without crossing production trust boundaries.