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
Full-duplex omni models are transforming human--AI interaction from turn-based exchanges into continuous multimodal conversations in which speaking, listening, and reasoning unfold concurrently. Rather than viewing the model as a replacement for a human endpoint, we argue for a new perspective: the model is a stateful computational middlebox inside a human-centered feedback loop, with network transport, model serving, and user playback jointly shaping how the interaction evolves. This perspective breaks the traditional boundaries among stages designed around local objectives. Rather than optimizing them in isolation, an AI-native real-time stack should allow the state of each stage to shape the actions of the others. We explore three cross-stage coordination opportunities: network-aware inference scheduling, execution-aware transport prioritization, and playback control that accounts for both network and model variability. We are building Conflux to explore these ideas, and preliminary results show substantial improvements in response latency and playback deadline adherence under network degradation. More broadly, we call for an AI-native real-time communication stack that resolve the joint control problem spanning communication, computation, and playback.
HalluProp, a Propagation-aware Hallucination inference framework that estimates individual agent failures and emergent system-level hallucination risks before inter-agent interaction, and effectively complements post-hoc methods, highlighting the potential of pre-hoc risk inference for building more reliable multi-agent systems.
Shi Lin, Chenpei Wang, Peng Qian et al.· 0 citations