The paper introduces a multi‑agent framework that transforms natural‑language MLOps tasks into verified repositories and operational cloud deployments. It uses a stateful Graph Orchestrator to coordinate agents for repository generation, review, execution, verification, release, and monitoring, ensuring lifecycle transitions only occur when supported by verifiable evidence. The framework, implemented on Google Cloud Platform, demonstrates prevention of unsupported transitions and drives each run toward a verified deployment or an auditable failure.
By Sagar Srinivas Sakhinana, Venkataramana Runkana
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:2607. 07052v1 Announce Type: cross Abstract: AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems.
By Arun Malik
arXiv:2606. 19382v1 Announce Type: cross Abstract: While LLM-powered agents offer end-to-end automation for industrial asset lifecycles, real-world Industry 4.
By Kanishk Kushwaha, Vikrant Vinod Bansode, Harsh Vardhan, Dhaval C. Patel
arXiv:2607. 08010v1 Announce Type: cross Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request.
By Kalle Kujanp\"a\"a, Ning Liu, Shahnawaz Alam, Yeshwanth Reddy Sura, Tianyu Yang, Kristina Klinkner, Shervin Malmasi
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