arXiv AI
Sep 17

Who Audits Whom, on What Substrate, with What Evidence? An Independence-Graded Audit Protocol for Agentic AI

The paper proposes an independence‑graded audit protocol for agentic AI systems, arguing that independence should be evaluated along three orthogonal axes: principal independence, substrate independence, and evidence independence. It introduces a seven‑step protocol based on the beta‑factor model from reliability engineering, demonstrates its application through a structural detectability analysis and a Monte Carlo study, and maps the framework to relevant regulatory standards such as the EU AI Act, ISO/IEC 42006, and UK public‑sector guidance.

By Mohamed Chahine Ghanem
arXiv AI
Aug 25

HANSARD: A Reference Architecture for Forensic Readiness, Runtime Witnessing, and Graded Attribution in Autonomous Multi-Agent AI Systems

arXiv:2608.22512v1 Announce Type: new Abstract: Autonomous multi-agent systems nowadays act in finance, software supply chains, and security operations. Already, the first largely AI-orchestrated int...

By Christos Sardianos, Iliana Pla, Vasilis Efthymiou, Iraklis Varlamis, Thomas Lagkas, Panagiotis Sarigiannidis, Georgios Th. Papadopoulos
arXiv AI
5d ago

Auditable Claims about AI Agents

The paper "Auditable Claims about AI Agents" argues that for a claim about an AI agent to be verifiable, it must explicitly state the agent’s policy, scope, the records that would confirm the claim, and who is responsible for those records. It extends the Auditable Agents framework by adding conditions of independent record coverage, action‑specific authorization, and completeness beyond integrity, and demonstrates the approach with a claim‑check table applied to six common claims. The authors provide a worked example and practical guidance for operators, buyers, auditors, and standard setters.

By Yue Zhao, Jiate Li, Li Li, Yi Nian, Jinbo Liu, Xiaolin Zhou, Xiyang Hu
arXiv AI
Jun 2

POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems

arXiv:2606. 02282v1 Announce Type: new Abstract: Orchestrating Large Language Models into Multi-Agent Systems (LLM-MAS) has unlocked remarkable reasoning capabilities, yet emergent failures and hallucinations that resist characterisation block their deployment in safety-critical domains -- a gap made legally untenable by emerging AI regulation.

By I\~naki Dellibarda Varela, R. Sendra-Arranz, Pablo Romero-Sorozabal, J. M. Valverde-Garc\'ia, Annemarie F. Laudanski, \'Alvaro Guti\'errez, Eduardo Rocon, Manuel Cebrian