A new research paper proposes an organizational framework for governing agentic AI systems, addressing the limitations of traditional software risk management. The paper introduces a seven-dimension profile to distinguish between software-engineering, hybrid, and AI-native teams, along with a six-cluster taxonomy for failure modes. It highlights that risk coverage degrades significantly as teams transition to AI-native operations, with the most severe failures occurring at the organizational boundary where probabilistic AI outputs interact with deterministic systems. AI
IMPACT This research offers a new framework for managing risks associated with AI-native engineering teams, potentially improving the governance and safety of agentic systems.
RANK_REASON The cluster contains a research paper published on arXiv detailing a new framework for AI risk management.
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- alphaXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- ISO/IEC 42001
- Laxmipriya Ganesh Iyer
- NIST AI RMF
- OWASP
- ScienceCast
- Agentic System Governance
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