Telukunta et al. argue that enterprise governance of agentic AI fails because it applies a single deterministic toolkit across four distinct scales of stochastic agency. They propose the CASE framework, which maps specific disciplines to each scale: control theory for individual agents, complex adaptive systems for agent collectives, supervisory cybernetics for human-agent teams, and engineering operations for fleet-level automation. The authors derive cross-layer coupling conditions and introduce a "zero-touch deployment paradox," where improved deployment automation mechanically outpaces static oversight capacity. Validating the model through three studies of public data, they identify an "Emergence Gap," noting that while 82% of production failures involve multi-layer trajectories, no major tooling provides full coverage for the complex adaptive layer and no public enterprise deployment evidences any interaction-level governance.
- The framework formalizes oversight requirements as testable engineering inequalities, specifically converting the Law of Requisite Variety into a verifiable design condition for compliance with EU AI Act Article 14.
- A non-compensatory maturity index is used, where the weakest layer carries majority weight, effectively preventing high scores in operational layers from masking critical gaps in interaction-level governance.
- Autonomy is redefined as a controlled variable modulated by a "decision quality" error budget, allowing for the systematic expansion or contraction of agent scope based on live performance data.
- The framework formalizes oversight requirements as testable engineering inequalities, specifically converting the Law of Requisite Variety into a verifiable design condition for compliance with EU AI Act Article 14.
- A non-compensatory maturity index is used, where the weakest layer carries majority weight, effectively preventing high scores in operational layers from masking critical gaps in interaction-level governance.
- Autonomy is redefined as a controlled variable modulated by a "decision quality" error budget, allowing for the systematic expansion or contraction of agent scope based on live performance data.
