arXiv:2604. 05485v2 Announce Type: replace Abstract: LLM agents call tools, query databases, delegate tasks, and trigger external side effects.
By Yi Nian, Aojie Yuan, Haiyue Zhang, Jiate Li, Li Li, Xiyang Hu, Hua Wei, Xiongye Xiao, Chaowei Xiao, Yue Zhao
The article discusses how AI agents are transforming software development by autonomously creating pull requests, selecting dependencies, refactoring code, interpreting test failures, and managing infrastructure across the SDLC. It highlights the lack of accountability for these transient processes, noting that traditional tools like git logs and governance frameworks assume human decision‑making, which no longer applies. To address this, the authors propose Agentic Process Evidence (APE), a signed, tamper‑evident, machine‑readable attestation that links AI agent sessions to their outcomes, enabling policy‑as‑code controls and human oversight only when necessary.
By Yuval Kainan, Carmit Hershman
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
The paper argues that as AI systems increasingly generate code, the bottleneck has shifted to supervising these systems, revealing a vocabulary gap between cybernetic coordination (actions aligning with the world) and epistemic coordination (understanding that can be verified). It critiques current oversight that merely approves outputs, proposing instead that every consequential choice by an agent must include a retrievable condition explaining why it was made, enabling third‑party verification. The authors illustrate this with three delegation episodes, introduce a two‑part reconstruction test, and propose the ORRCF convention to embed such conditions in all recorded decisions.
By J\'er\'emie Lumbroso
The paper introduces SARA, a framework that separates action induction from runtime authorization in tool‑augmented LLM agents. By treating these as distinct roles, SARA uses an Action Probe to record action provenance and only authorizes tool calls that align with the user objective and past successful executions. Experiments on AgentDojo and AgentDyn show that SARA reduces action‑to‑side‑effect risk to below 0.63% while preserving task performance.
By Xiaokun Guo, Zhen Xu, Dongdong Huo, Yanqiu Zhang, Wei Wang, Qinfu Yang, Dongjin Yu, Yu Wang
The paper introduces Aegis, a runtime governance system for agentic AI that treats model outputs as action proposals and mediates them through a trusted decision layer before tool execution. Aegis evaluates proposals against active policy, resolves provenance server‑side, fails closed under uncertainty, and routes selected cases through a Senate‑style settlement process. In a sandbox evaluation across 6,300 rows, Aegis prevented all governed mock‑tool applications and risky side‑effect completions, preserving provenance and quorum evidence for all settled cases.
By Adam Mazzocchetti