← Back to all news
arXiv AI By Sagar Srinivas Sakhinana, Venkataramana Runkana

Towards Agentic Cloud Engineering: Graph and Loop Engineering with a Zero-Trust Agent Harness

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Sep 1

Forward-Deployed Full-Stack Engineering for Autonomous Cloud MLOps

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
agents
More like this →
arXiv AI
Jul 15

Agentic Service-Oriented Computing: A Manifesto for the Next Frontier of Service-Oriented Computing

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
llmsagents
More like this →
arXiv AI
Jun 17

Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization

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
llmsagentsbenchmarksdata-engineering
More like this →
arXiv AI
Aug 18

Specifying AI-SDLC Processes: A Protocol Language for Human-Agent Boundaries

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
agentsbenchmarks
More like this →