CASE Framework: Governing Enterprise Agentic AI with Multi-Disciplinary Control
A new academic paper introduces the CASE framework, a multi-disciplinary control architecture for governing enterprise agentic AI. The paper argues that current governance approaches, typically based on DevSecOps, are insufficient because they apply a single discipline to all scales of agency. Instead, the authors propose that agentic AI governance comprises four distinct problems, each with a mature governing science: Control theory for individual agents, complex adaptive systems theory for agent collectives, supervisory cybernetics for human-agent teams, and engineering operations for fleets. The framework formalizes each layer and derives cross-layer coupling conditions, including a zero-touch deployment paradox. The paper is available on arXiv under the identifier 2608.10153.
Key facts
- Paper introduces CASE framework for governing enterprise agentic AI.
- CASE stands for Control, Adaptive systems, Supervisory cybernetics, and Engineering operations.
- Argues that current DevSecOps approaches are insufficient for all scales of agency.
- Assigns Control theory to individual agents, with intent as setpoint, guardrails as feedback, evaluation as observation.
- Applies complex adaptive systems theory to agent collectives, noting emergence makes single-agent assurance non-compositional.
- Uses supervisory cybernetics for human-agent teams, citing the Law of Requisite Variety showing unaided human oversight fails structurally.
- Extends error budgets to decision quality for fleets, making autonomy a controlled variable.
- Paper is available on arXiv with identifier 2608.10153.
- Derives cross-layer coupling conditions, including a zero-touch deployment paradox.
Entities
Institutions
- arXiv