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STAROps

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Headlines · 7

  1. Alibaba Cloud details AgentCore enterprise-agent governance stack

    Alibaba Cloud’s Hangzhou salon for enterprise-agent construction and governance drew more than 100 technical practitioners, according to a Juejin event recap. Sessions covered AgentCore, AgentLoop evaluation, STAROps, UModel, Agent Framework, and an AI gateway, with hands-on exercises for building, managing, and collaborating with agents.

    掘金 · 人工智能 · 🔥 0
  2. Diagnosing Slow Elasticsearch Search with STAROps

    A Juejin tutorial demonstrates how engineers can use the STAROps Elasticsearch 智能助手 to inspect per-node CPU, query load, and shard distribution during a search slowdown. Its worked example shows how a Green cluster and healthy average CPU can conceal sustained pressure on one node, while emphasizing that slow logs and thread-pool rejection deltas are still needed to establish the root cause.

    掘金 · 人工智能 · 🔥 0
  3. Alibaba Cloud pairs STAROps with OSS Agent for cross-domain troubleshooting

    An article describes a troubleshooting workflow in which STAROps correlates signals across technical domains and OSS Agent examines Object Storage Service configurations, access logs, and usage data. Three worked examples attribute 503 SlowDown errors to a partition hotspot caused by sequential object-name prefixes, 403 AccessDenied errors to an explicit deny in a Bucket Policy, and a 17-fold traffic increase to an enabled hotlink-protection setting that allowed empty Referer values. The article presents these diagnoses and recommended remedies as demonstrations and does not provide independent test results.

    掘金 · 人工智能 · 🔥 0
  4. Alibaba Cloud Observability Roundup Highlights Agentic Operations

    An October 2026 Alibaba Cloud observability roundup lists articles on AgentCore AI-native operations, STAROps and ROS Agent automation, SLS Session Store for self-evolving agents, AgenticOps open-source work, and AgentLoop auditing for Dify applications. Other topics include Session Replay, SLS mask, SLS Agent Skills, Cloud Monitor 2.0, automatic index discovery, STAROps upgrades and front-end troubleshooting, and AI SSE probing for streaming experiences. The roundup provides limited detail about individual releases, versions, and feature changes.

    掘金 · 人工智能 · 🔥 0
  5. Alibaba Cloud schedules AgentCore enterprise-agent salon in Hangzhou

    Alibaba Cloud announced that its AgentCore enterprise-agent construction and governance salon will visit Hangzhou on November 6, 2026. The event will feature technical presentations, customer examples, and hands-on exercises covering agent hosting, unified management, multi-agent collaboration, access controls, auditing, cost governance, observability, and evaluation. Alibaba Cloud also cited a Shanghai customer survey identifying evaluation and optimization, multi-agent integration, and security and permission isolation as leading deployment challenges.

    掘金 · 人工智能 · 🔥 0
  6. Alibaba Cloud details AI-native operations with AgentCore and STAROps

    Alibaba Cloud described an AI-native operations implementation built around UModel, STAROps, AgentCore, and AgentLoop for troubleshooting its own agent platform. UModel links agents, sessions, sandboxes, logs, metrics, and events, while STAROps hosts operational digital employees and AgentLoop reviews diagnostic evidence and results. The report includes a production investigation that traced repeated Sandbox rebuilding to a Runtime startup-configuration failure before connecting the findings to source analysis.

    掘金 · 人工智能 · 🔥 0
  7. Alibaba Cloud details seven showcase teams in AI-native R&D experiment

    InfoQ Chinese recaps Alibaba Cloud's Yunqi conference forum on AI-native R&D organizations: over the past three months the company's CTO engineering line ran an AI Native exploration across a 6,000-plus-person R&D organization, using a "north star, blank space, sandbox" approach to select seven showcase teams that are in production, have observable business results and could be reused. Alibaba Cloud R&D vice president Tang Hong framed the agent computing paradigm as "model plus Harness plus Context," and argued that agents entering the production pipeline is the real dividing line, with AI Native passing through tool-native, workflow-native and organization-native stages. Reported results include one project cutting end-to-end processing from two hours to five minutes, another shortening release cycles from 40 days to 2 days, all alerts getting a first-pass agent analysis, STAROps accumulating more than 1,400 evaluation items, and an Agentic SOC team reducing average requirement cycles from 50.9 to 13.8 days; the authors state these figures do not represent overall performance and that reuse across teams still needs validation.

    InfoQ 中文 AI&大模型 · 🔥 0

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