Presents a declarative governance model to manage AI agent behavior in regulated environments, suggesting improved compliance and reduced risks.
AI agents combine models, prompts, and tools into a composite system with a behavioral capability surface that is rarely declared in a single, reviewable artifact. As these systems move into regulated, customer-facing, and business-critical environments, the absence of a single, canonical mechanism for declaring, reviewing, and binding agent capability at deployment boundaries creates material governance, compliance, and operational risk. As a result, undeclared capability expansion, environment drift, and limited reproducibility frequently occur at promotion boundaries. This paper introduces BehaviorSpec, a declarative governance model for managing an agent’s behavioral capability surface across staging and production environments. BehaviorSpec requires two artifacts: (1) a mandatory behavior.intent, which defines declared purpose, scope, tool permissions, model policy, constraints, and promotion requirements, and (2) a mandatory behavior.lock, generated at promotion time, which binds the approved intent to immutable artifact identities such as model versions and tool releases. Together, these artifacts enforce a promotion invariant: no agent may be deployed into a controlled environment unless its declared behavioral intent has been reviewed, approved, and cryptographically bound to the exact runtime artifacts.
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Rick Buonincontri (2026) studied this question.
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