发表机构
Tata Research Development and Design Centre（塔塔研究开发设计中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出Agentic Cloud Workflow Engineering框架，将自然语言智能体云工程任务转为验证后的代码仓库与云部署，划分三类工程关注点并在Google Cloud验证，为多类智能体云工程领域提供统一架构。
AI 中文摘要
智能体AI正推动基于云的工作流，其中自主智能体会基于操作状态进行推理，调用授权工具，修改软件与基础设施，部署服务，验证执行结果，并在长周期多步骤任务中自适应调整。设计此类工作流需要明确的工作流推进机制、受限执行机制、故障恢复机制及可验证的完成机制。我们提出了Agentic Cloud Workflow Engineering（智能体云工作流工程），这是一种智能体AI框架，可将自然语言形式的智能体云工程任务转化为经过验证的代码仓库和已验证的云操作部署，用于自动化基于云的智能体工作流。该框架划分了三个互补的关注点：图工程指定长周期工作流推进及依赖验证的转换；循环工程提供受限诊断、修复或重新规划、重试及重新验证；智能体管控器工程通过身份、授权、策略范围的能力、隔离及运行时保障措施，强制实施零信任执行。工作流推进与完成需要机器可检查的仓库、部署及运行时证据，且恢复过程受明确的操作边界和终止标准约束。我们在Google Cloud上实例化该框架，并对仓库完整性、受控执行、证据门控推进、操作部署及受限恢复进行评估。实验结果表明，在受限恢复下，执行会以经过验证的操作云部署或可审计的终端故障终止。该框架为覆盖Agentic DevOps、Agentic CloudOps、Agentic SRE/AIOps、Agentic SecOps、Agentic DataOps、Agentic MLOps/LLMOps、AgentOps、Agentic RAG/GraphRAG及相关云工程领域的基于云的工作流提供了统一的工程架构。
英文摘要
Agentic AI is enabling cloud-based workflows in which autonomous agents reason over operational state, invoke authorized tools, modify software and infrastructure, deploy services, verify execution outcomes, and adapt across long-horizon, multistep tasks. Engineering such workflows requires explicit mechanisms for workflow progression, constrained execution, failure recovery, and verifiable completion. We present Agentic Cloud Workflow Engineering, an agentic AI framework that transforms natural-language agentic cloud-engineering tasks into validated code repositories and verified operational cloud deployments for automating cloud-based agentic workflows. The framework separates three complementary concerns: graph engineering specifies long-horizon workflow progression and verification-dependent transitions; loop engineering provides bounded diagnosis, repair or re-planning, retry, and re-verification; and agent harness engineering enforces zero-trust execution through identity, authorization, policy-scoped capabilities, isolation, and runtime safeguards. Workflow progression and completion require machine-checkable repository, deployment, and runtime evidence, with recovery constrained by explicit operational bounds and termination criteria. We instantiate the framework on Google Cloud and evaluate repository completeness, controlled execution, evidence-gated progression, operational deployment, and bounded recovery. Experimental results show that executions terminate with either a verified operational cloud deployment or an auditable terminal failure under bounded recovery. The framework provides a unified engineering architecture for cloud-based workflows spanning Agentic DevOps, Agentic CloudOps, Agentic SRE/AIOps, Agentic SecOps, Agentic DataOps, Agentic MLOps/LLMOps, AgentOps, Agentic RAG/GraphRAG, and related cloud-engineering domains.
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