arXiv:2607. 22611v1 Announce Type: new Abstract: The deployment of autonomous AI agents in production infrastructure introduces fundamental security challenges that traditional role-based access control (RBAC) models cannot address.
By Arun Malik, Deepal Jayasinghe, Bradley Klemick, Prachi Shah, Nitish Talasu, Vineet Tushar Trivedi
arXiv:2609.38652v1 Announce Type: new
Abstract: Agentic AI systems increasingly rely on cloud-hosted large language models for planning, tool use, and iterative execution, raising concerns about API...
By Yizhou Han, Di Wu, Dhananjay Saikumar, Blesson Varghese
arXiv:2607. 25076v1 Announce Type: new Abstract: Every major wave of platform software follows the same arc: an initial period of experimentation with competing frameworks and ad-hoc implementations, followed by the articulation of a small set of stable abstractions with well-defined semantics, and finally consolidation around those abstractions into a platform that applications can portably target.
By Gosia Steinder, Hubertus Franke
arXiv:2606. 12835v1 Announce Type: cross Abstract: The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action.
By Quanyan Zhu
Stateful Runtime for Agents in Amazon Bedrock brings persistent orchestration, memory, and secure execution to multi-step AI workflows powered by OpenAI.
The paper introduces Agentic Cloud Workflow Engineering, a framework that converts natural‑language agentic cloud‑engineering tasks into validated code repositories and verified cloud deployments. It separates graph engineering for long‑horizon workflow progression, loop engineering for bounded diagnosis and recovery, and agent harness engineering for zero‑trust execution. Experiments on Google Cloud show that executions either produce a verified deployment or an auditable terminal failure within bounded recovery limits.
By Sagar Srinivas Sakhinana, Venkataramana Runkana