Agentic cloud management is emerging as a practice to automate laborious operations, minimize toil, and improve responsiveness. Despite the rapid development of autonomous management agents, we argue that the fundamental missing piece is a systems foundation to enable safe, effective operations across agents and between agents and human operators. In this paper, we advocate for the need of such a systems foundation and share our efforts on developing CloudWeaver, an agentic management substrate that works across existing cloud-user interfaces and future agent-native interfaces. Specifically, we discuss how CloudWeaver (1) scopes the context of individual agent sessions with local views of cloud resources and (2) coordinates concurrent management operations on shared cloud resources. CloudWeaver offers strong safety guarantees and attributable feedback in the presence of conflicting intents, while preserving concurrency between independent operations. We validate CloudWeaver using a representative Azure API workload.
Minghao Li, Ziqian Liu, Ziyu Mao et al.· 0 citations
Coding agent infrastructure is shifting from harness engineering toward loop engineering as coding agents are deployed for sustained long-horizon software development. Existing benchmarks often center on localized tasks or end-state outcomes, offering limited insight into sustained execution. We introduce LOOPSBENCH, a long-horizon benchmark for loop engineering in coding agent evaluation. Each task is a dependency DAG over separately testable development units with source-evidenced prerequisite edges. LOOPSBENCH comprises 112 tasks from authentic sources spanning 8 programming languages and 9 domains. Its flow-aware runtime releases tests along the ready frontier and retains completed nodes as regression obligations. We evaluate frontier coding agents paired with widely used loop implementations. The strongest configuration, Opus-4.7 with Claude Code and outer continuation, resolves 25.00% of tasks. Recorded plans recover only part of the source-recovered prerequisite DAG, and regression events remain visible across the evaluated loop profiles. We open source the benchmark data and code, including all tasks, more than 5,300 development units, and executable tests, at microsoft/Loopsbench.
This work presents Change2Task, a system grounded in repository history that converts merged pull requests into verified tasks on healthy modern revisions of the same repository, and provides executable data for coding agent training and evaluation while reducing repeated environment setup, storage, and task construction effort.
Haomin Qi, Xingliang Wang, Xuanqi Gao et al.· 0 citations