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
arXiv:2606. 17915v1 Announce Type: cross Abstract: Big-Data-as-a-Service (BDaaS) platforms require re liable automation across data ingestion, cleaning, feature engi neering, model development, deployment, and post-deployment monitoring.
By Aueaphum Aueawatthanaphisut, Badri Raj Lamichhane
arXiv:2604. 10311v2 Announce Type: replace Abstract: Artificial Intelligence (AI) models, encompassing both traditional machine learning (ML) and more advanced approaches such as deep learning and large language models (LLMs), play a central role in modern applications.
By Fabio Porto, Eduardo Ogasawara, Gabriela Moraes Botaro, Julia Neumann Bastos, Augusto Fonseca, Esther Pacitti, Patrick Valduriez
The paper introduces AOI (Autonomous Operations Intelligence), a multi‑agent framework that treats automated site reliability tasks as a structured trajectory learning problem under security constraints. It combines a trainable diagnostic system using Group Relative Policy Optimization, a read‑write separated execution architecture, and a Failure Trajectory Closed‑Loop Evolver that turns unsuccessful runs into corrective supervision. On the AIOpsLab benchmark, AOI achieves significant performance gains, including a 66.3% best@5 success rate and improved average metrics on unseen fault types.
By Pei Yang, Wanyi Chen, Asuka Yuxi Zheng, Xueqian Li, Xiang Li, Haoqin Tu, Jie Xiao, Yifan Pang, Dongdong Zhang, Fuqiang Li, Alfred Long, Lynn Ai, Eric Yang, Bill Shi, Yifan Chen, Shuhan Sun
The paper introduces two zero‑trust frameworks for cloud data engineering and analytical processing. The first, Zero‑Trust Agentic Data Engineering, automatically generates, deploys, and verifies complete data‑engineering solutions from natural‑language tasks, requiring evidence from repositories, deployments, runtimes, and policies. The second, Zero‑Trust Agentic OLAP, combines governed data preparation with verified online analytical processing, allowing production promotion only after rigorous validation and evidence‑bound approval, and ensuring analytical outputs are released only after same‑snapshot execution, exact result equivalence, deterministic grounding, and reflection. Both frameworks rely on three core abstractions—graph engineering for evidence‑gated workflow structure, loop engineering for bounded recovery, and agent‑harness engineering for zero‑trust execution—and are evaluated under nominal execution, controlled failures, bounded recovery, and policy‑constrained conditions to measure verified completion, recovery, authorization enforcement, production promotion, and verified OLAP execution.
By Sagar Srinivas Sakhinana, Venkataramana Runkana
AegisFlow is a multi‑agent AI framework that automates the detection and remediation of failures in data pipelines, using a Watchdog agent for telemetry and a Repair agent that generates, tests, and deploys code patches via large language models. It employs a non‑intrusive Parallel Shadow Patching approach based on the MAPE‑K loop to validate patches in digital twin environments. Experiments across five failure scenarios show a 98.1% reduction in mean time to repair—from 170 minutes to 3.2 minutes—and a 92% patch success rate, freeing up 98% of data engineering on‑call time for innovation.
By Muhammad Bilal Awan, Zubair Hussain, Abdul Shahid