AI-GRACE: A Use-Case Operationalization Framework for Agentic AI: From Organizational Objectives and Obligations to Deployment Capabilities and Architecture
arXiv cs.AIen
arXiv:2609.21192v1 Announce Type: new Abstract: Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate, control, and observe for a use case to deliver its intended outcome while meeting applicable obligations. This paper proposes AI-GRACE (Agentic Intelligence-Governance, Risk, Assurance, Controls, and Evidence) as a use-case operationalization framework connecting organizational governance with technical implementation. The proposal draws on professional observations and a purposive synthesis of standards and literature, using design science to frame the method contribution and situational method
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Verktyg
- Forskning
- Agenter
Related AI news
- A researcher used GPT-6 Astra to decipher a WWI German radio transmission from 1918, one of the 50 famous unsolved ciphers listed on a German science blog (prinz)Techmeme · September 21, 2026
- Styr AI-agenter som om de vore anställda – men låtsas inte att de är människorComputer Sweden · September 21, 2026
- DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-RefinementarXiv cs.AI · September 21, 2026
- GVPO++: Group Variance Policy Optimization for LLM Post-Training and On-Policy DistillationarXiv cs.AI · September 21, 2026
- Driving on Registers, Reasoning on Risk: Risk-Aware Occupancy for Register-Based End-to-End Autonomous DrivingarXiv cs.AI · September 21, 2026
- RBS-Attention: Radius-Bounded Sparse Prefill for Long-Context Large Language ModelsarXiv cs.AI · September 21, 2026