CASE Framework: Governing Enterprise Agentic AI with Multi-Disciplinary Control.
Key takeaways
- Traditional DevSecOps is insufficient for governing complex, emergent agentic AI systems.
- The CASE framework offers a multi-disciplinary approach, breaking governance into four distinct problems.
- Effective governance requires addressing individual agents, collectives, human-agent teams, and entire fleets.
- There is a significant "Emergence Gap" where current tools and practices fail to manage emergent behaviors.
Who benefits
Summary
The CASE framework proposes a multi-disciplinary control architecture to govern enterprise agentic AI, addressing the rapid deployment of autonomous agents without adequate governance. It breaks down AI governance into four problems, each with a mature governing science, to manage individual agents, collectives, human-agent teams, and fleets.
Why it matters
Professionals deploying or managing AI agents need a robust framework to ensure governance, mitigate risks, and comply with emerging regulations like the EU AI Act. This framework offers a structured, scientific approach to manage the complexity and emergent behaviors of agentic AI systems.
How to implement this in your domain
- 1Assess current AI agent deployments against the CASE framework's four layers (individual, collective, human-agent team, fleet) to identify governance gaps.
- 2Integrate control theory principles for individual agent guardrails and intent alignment within your AI engineering practices.
- 3Develop strategies for monitoring and managing emergent behaviors in agent collectives, recognizing that single-agent assurance is insufficient.
- 4Implement supervisory cybernetics principles to design effective human-agent teaming structures, ensuring humans can maintain requisite variety in oversight.
- 5Extend engineering operations practices to include decision quality as a controlled variable for agent fleets, moving beyond traditional error budgets.
Original post by Srinivas Telukunta, Georgios Nektarios Lilis, Lucio Baron
"arXiv:2608.10153v1 Announce Type: new Abstract: Enterprises are deploying autonomous AI agents faster than they can govern them, and prevailing approaches stretch a single discipline, typically DevSecOps built for deterministic automation, across every scale of agency. We argue t…"
View on XOriginally posted by Srinivas Telukunta, Georgios Nektarios Lilis, Lucio Baron on X · view source
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