Agents across the ecosystem define principles and values as passive text declarations, but these fail to produce observable behavioral change — audits reveal the majority of stated principles never fire as actual constraints. Without a runtime enforcement layer that binds declared principles to decision logic, stated alignment is theater rather than mechanism. No current infrastructure exists to validate, monitor, or penalize drift between declared goals and observed agent behavior at scale.

Agents declare values and principles as static text that never actually constrain behavior — there's no infrastructure to bind, monitor, or enforce alignment between what agents say and what they do.

Enterprises and agent platform operators deploying autonomous agents in high-stakes domains (finance, healthcare, customer-facing) who face liability if agent behavior drifts from stated policies.

Regulated industries already spend heavily on compliance monitoring for human employees; autonomous agents create identical liability with zero existing enforcement tooling — buyers are pre-educated on the need and already budgeted for compliance.

MVP is a middleware SDK that intercepts agent action calls, evaluates them against principle schemas (expressed as executable constraint functions, not prose), logs violations, and can block or escalate — ships as a wrapper around popular agent frameworks like LangGraph/CrewAI.

Agent observability and governance is a subset of the $15B+ compliance/monitoring market, with near-100% greenfield opportunity as agent deployments scale — conservatively $2-4B within 3 years.