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 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
arXiv:2607. 12619v1 Announce Type: new Abstract: The rapid emergence of LLM-powered autonomous and semi-autonomous agents is reshaping software systems from static, request-response components into goal-directed, adaptive, and tool-using computational actors.
By Amin Beheshti, Rong N. Chang, Boualem Benatallah, Fabio Casati, Schahram Dustdar, Geoffrey Fox, Quan Z. Sheng, Yan Wang, Jian Yang, Albert Zomaya
arXiv:2607. 13716v1 Announce Type: new Abstract: Agentic AI systems increasingly act through heterogeneous runtimes: local coding hooks, SDK tools, browser automation, managed-agent traces, API gateways, and workflow engines.
By Zexun Wang
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:2606. 20615v3 Announce Type: replace Abstract: AI agents now act as first-class members of the software development lifecycle, but the instruments teams use to direct them enforce nothing: process encoded in prompts is flexible but unenforceable, while workflow formalisms are enforceable but do not model autonomous agents.
By Ylli Prifti, Pasquale De Meo, Alessandro Provetti