{"slug":"agent-substrate-advancing-kubernetes-for-agents-d4450502","url":"https://silinex.xyz/wire/agent-substrate-advancing-kubernetes-for-agents-d4450502","json_url":"https://silinex.xyz/wire/agent-substrate-advancing-kubernetes-for-agents-d4450502.json","md_url":"https://silinex.xyz/wire/agent-substrate-advancing-kubernetes-for-agents-d4450502.md","headline":"Agent Substrate: Advancing Kubernetes for Agents at Scale","headline_zh":"Agent Substrate：面向大规模智能体的 Kubernetes 演进","title":"Agent Substrate: Advancing Kubernetes for Agents at Scale","summary":"According to public reporting via NVIDIA Developer Blog, aI agents spend most of their time idle, waiting on a human prompt, a tool result or a model response.","summary_zh":"据NVIDIA开发者博客的公开报道，AI智能体大部分时间处于空闲状态，等待人类提示、工具返回结果或模型响应。","date_published":"2026-11-10T15:00:00.000+00:00","date_modified":"2026-11-12T06:24:48.522+00:00","author":{"type":"Organization","name":"NVIDIA Developer Blog","url":"https://developer.nvidia.com/blog/agent-substrate-advancing-kubernetes-for-agents-at-scale/"},"publisher":{"name":"Silicon Nexus","url":"https://silinex.xyz/"},"category":"ai","language":"en","source_url":"https://developer.nvidia.com/blog/agent-substrate-advancing-kubernetes-for-agents-at-scale/","source_name":"NVIDIA Developer Blog","key_facts":["aI agents spend most of their time idle, waiting on a human prompt, a tool result or a model response.","Kubernetes wasn&rsquo;t built for that.","Running one Pod per session holds memory for sessions that aren&rsquo;… t or a model response.","Kubernetes wasn’t built for that."],"key_facts_zh":["AI 智能体大部分时间都处于空闲状态，等待人类提示、工具结果或模型响应。","Kubernetes 并非为此而设计。","每个会话运行一个 Pod 会为那些并不存在的会话保留内存……或模型响应。","Kubernetes 并非为此而设计。"],"body_md":"## Wire brief\n\nAccording to public reporting via NVIDIA Developer Blog, aI agents spend most of their time idle, waiting on a human prompt, a tool result or a model response.\n\nIn related context, kubernetes wasn&rsquo;t built for that. Running one Pod per session holds memory for sessions that aren&rsquo;… t or a model response. Kubernetes wasn’t built for that.\n\nReporting also notes that rL post-training hits the same wall. Agent Substrate, now donated to the Cloud Native Computing Foundation (CNCF) Sandbox, is an open-source, security-hardened, zero-trust runtime for sandboxed agents, engineered to safely run millions of agents and tools with 10x higher density than standard containers. It multiplexes many sessions over a small pool of worker Pods. Idle sessions are checkpointed, memory and filesystem included, and restored onto whichever worker is free when the next request arrives.\n\nThis Silicon News Wire item is a paraphrased brief for readability; full wording and context remain on the origin page.\n\n## Source & notice\n\nThis Silicon News Wire brief paraphrases public reporting from **NVIDIA Developer Blog** for carbon readers and silicon citation. **It is not a verbatim full-text reprint.**\n\nRead the original: [Agent Substrate: Advancing Kubernetes for Agents at Scale](https://developer.nvidia.com/blog/agent-substrate-advancing-kubernetes-for-agents-at-scale/)\n","body_md_zh":"## 电讯正文\n\n据NVIDIA开发者博客的公开报道，AI智能体大部分时间处于空闲状态，等待人类提示、工具返回结果或模型响应。\n\n相关背景是，Kubernetes并非为此而设计。每个会话运行一个Pod，会为那些可能永远不再活跃的会话持续占用内存。Kubernetes并不是为这种场景构建的。\n\n报道还指出，强化学习后训练也撞上了同一堵墙。Agent Substrate现已捐赠给云原生计算基金会（CNCF）沙箱，它是一个开源、安全加固、零信任的沙箱化智能体运行时，专为安全运行数百万个智能体和工具而设计，密度比标准容器高10倍。它将大量会话多路复用到一小池工作Pod上。空闲会话会被检查点保存（包括内存和文件系统），并在下一个请求到来时恢复到任意空闲的工作节点上。\n\n## 来源与声明\n\n本电讯由 Silicon News Wire 依据 **NVIDIA Developer Blog** 公开报道做要点改写与润色，便于碳基阅读与硅基引用；**不是原文全文转载**。\n\n阅读原文：[Agent Substrate：面向大规模智能体的 Kubernetes 演进](https://developer.nvidia.com/blog/agent-substrate-advancing-kubernetes-for-agents-at-scale/)\n","observations":{"human":18,"bot":5,"crawlers":[{"crawlerId":"AhrefsBot","hits":3,"lastSeenAt":"2026-11-11T04:52:13.956Z"},{"crawlerId":"ClaudeBot","hits":2,"lastSeenAt":"2026-11-12T12:50:13.219Z"}],"last_seen_at":"2026-11-12T12:50:13.219Z","note":"Counts are request logs classified by User-Agent. User-Agents can be spoofed. This is not proof of search-engine or LLM inclusion."}}