{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-08-11T09:21:12.743Z","headline":"LangChain 如何为 Kubernetes 构建自主 SRE 智能体","description":"LangChain 基于 Deep Agents 为 Kubernetes 部署构建了自主 SRE 智能体，可自动执行运维任务，并对变更操作引入人工审批机制。该智能体使用 LangSmith 进行全链路追踪，并通过 evals 评估系统性能，兼顾自动化效率与运维安全。","url":"https://www.aioga.com/news/cmsgb0jc7028uro3pnxg6blix/","mainEntityOfPage":"https://www.aioga.com/news/cmsgb0jc7028uro3pnxg6blix/","datePublished":"2026-08-05T16:24:31.000Z","dateModified":"2026-08-05T16:24:31.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://www.langchain.com/blog/how-we-build-an-autonomous-sre-agent-for-kubernetes-deployments","https://aihot.virxact.com/items/cmsgb0jc7028uro3pnxg6blix"],"canonicalUrl":"https://www.aioga.com/news/cmsgb0jc7028uro3pnxg6blix/","directAnswer":{"@type":"Answer","text":"Aioga 编辑摘要：LangChain 基于 Deep Agents 为 Kubernetes 部署构建了自主 SRE 智能体，可自动执行运维任务，并对变更操作引入人工审批机制。 Aioga 将其归入「技巧观点」方向，重点关注它对真实使用和行业竞争的影响。","url":"https://www.aioga.com/news/cmsgb0jc7028uro3pnxg6blix/","dateCreated":"2026-08-05T16:24:31.000Z","author":{"@type":"Organization","@id":"https://www.aioga.com/authors/aioga-editorial/#editorial-team","name":"Aioga Editorial Team","url":"https://www.aioga.com/authors/aioga-editorial/"}},"evidence":[{"@type":"CreativeWork","name":"langchain.com source article","url":"https://www.langchain.com/blog/how-we-build-an-autonomous-sre-agent-for-kubernetes-deployments","datePublished":"2026-08-05T16:24:31.000Z","provider":{"@type":"Organization","name":"langchain.com","url":"https://www.langchain.com/blog/how-we-build-an-autonomous-sre-agent-for-kubernetes-deployments"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.virxact.com/items/cmsgb0jc7028uro3pnxg6blix","datePublished":"2026-08-05T16:24:31.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.virxact.com/items/cmsgb0jc7028uro3pnxg6blix"}}],"aggregationSource":"LangChain：Blog（RSS）","originalPublisher":{"name":"langchain.com","url":"https://www.langchain.com/blog/how-we-build-an-autonomous-sre-agent-for-kubernetes-deployments"},"geoDeepAnswer":null,"article":{"id":"cmsgb0jc7028uro3pnxg6blix","slug":"cmsgb0jc7028uro3pnxg6blix","url":"https://www.aioga.com/news/cmsgb0jc7028uro3pnxg6blix/","title":"LangChain 如何为 Kubernetes 构建自主 SRE 智能体","title_en":"How we built an autonomous SRE agent for Kubernetes","summary":"LangChain 基于 Deep Agents 为 Kubernetes 部署构建了自主 SRE 智能体，可自动执行运维任务，并对变更操作引入人工审批机制。该智能体使用 LangSmith 进行全链路追踪，并通过 evals 评估系统性能，兼顾自动化效率与运维安全。","source":"LangChain：Blog（RSS）","sourceUrl":"https://www.langchain.com/blog/how-we-build-an-autonomous-sre-agent-for-kubernetes-deployments","aiHotUrl":"https://aihot.virxact.com/items/cmsgb0jc7028uro3pnxg6blix","publishedAt":"2026-08-05T16:24:31.000Z","category":"技巧观点","score":49,"selected":false,"articleBody":["How we build an autonomous SRE Agent for Kubernetes Deployments","I'm a Deployed Engineer at LangChain, and a good part of my job lives on top of Kubernetes. I maintain our internal self-hosted cluster (also where new self-hosted features land first, so our team can test them as they arrive), and I help customers stand up and upgrade their own self-hosted environments. Doing this requires a mental model of a live cluster deep enough that when ours goes down I can read the architecture, find the fault, and fix it. Doing that continuously, on top of a full-time job, is exhausting, and it isn't unique to me. Anyone who works on infrastructure knows these pains.","So we built an autonomous SRE Agent to reduce time to triage and time to remediation. The goal was to improve infrastructure reliability and reduce cognitive load on the team. We wanted it to triage Kubernetes health, propose fixes, and pull in a human only when a cluster or infrastructure changes need to happen. This post covers why, how we built it, why LangSmith makes it trustworthy, and the benefits we've seen.","Kubernetes emits a firehose of signals (pod phases, restart counts, HPA (Horizontal Pod Autoscaling) states, node conditions, warning events, deployment readiness, across dozens of namespaces) and almost no synthesis. On-call engineers use these signals to answer three questions: Is anything broken now (a crash loop, an OOM kill, zero ready endpoints); Is anything about to break (an HPA pinned at max, a single-replica service, :latest image tags); What do we do about it? Answering these questions well takes judgment, so it falls to infrastructure engineers or subject matter experts, but 90% of this work is mechanical triage that mostly comes back clean. This is the toil that burns people out and trains them to inadvertently skim past important alerts.","Proactive monitoring. A scheduler checks health every N minutes without waking the full agent. It collects raw cluster state through the Kubernetes Python client (zero LLM tokens), then makes one Claude Haiku call with forced tool-use to produce a structured health report that lands in Slack, sorted by severity.","On-demand investigation. When an issue needs diagnosis, the orchestrator fans out to specialized subagents in parallel: pod-inspector, scaling-analyzer, performance-analyzer, log-analyzer, security-auditor, reliability-auditor, and more. Each reads the cluster independently before it synthesizes one prioritized report.","The agent can read the entire cluster but change nothing on its own. Every write (scaling a deployment, restarting a rollout, patching an HPA) lives inside a single change-executor subagent, and each write tool is gated by a human-in-the-loop (HITL) interrupt. The agent proposes a remediation, a person approves, rejects, or edits, right from a Slack message. Read is autonomous and writing is always gated through HITL. It’s enforced structurally and mirrored by in-cluster RBAC (cluster-wide read, tightly scoped write).","Each choice below was a fork where the obvious path and the right path diverged.","The through-line in our architecture is to keep things as simple and cost-effective as possible. We spend tokens, model power, and increase network surface only where it improves agent outcomes for the infrastructure we’re managing. That's what makes the agent cheap enough to run every few minutes and safe enough to point at production.","Every decision in Part 2 runs as a LangSmith trace: the scheduled Haiku check, each subagent investigation, every read and proposed write. The Deep Agents and LangGraph pieces trace automatically; the scheduler's direct Anthropic calls are wrapped with @traceable.","Those labeled runs form the backbone of our evaluations. A misclassified pod or a missed OOM gets promoted into a LangSmith dataset with the correct answer attached, and every prompt or model tweak then runs against it with LLM-as-judge and code-based evaluators, so a change that regresses shows up as a red number and doesn't merge. The single-replica false positive above becomes a permanent test case for our eval suite.","The improvement loop above still relies on a human noticing undesirable behavior, finding the trace, and promoting it to a dataset. It’s time intensive for whoever maintains the agent. LangSmith Engine：https://docs.langchain.com/langsmith/engine-overview automates this manual work. It's like a proactive agent engineer that watches our tracing project in three stages.","Engine has grouped our traces into open issues we had not filed ourselves. One example is that the scheduled health check was collecting no utilization data at all. The collector queried nodes, pods, warning events, HPAs, and deployments, and the analysis step was a single forced-tool call with no tools available, so the model couldn’t fetch what the collector skipped. Every hourly report raised a capacity question it structurally could not answer and handed it back to us as a recommended action like \"check pod CPU/memory metrics\". The kubectl_top_pods and kubectl_top_nodes capability was already in the repo for the interactive agent and had never been wired into the scheduled path. Engine proposed wiring pod and node metrics into the collector, reusing the existing unit parsing rather than inventing new logic, and scoped the change to the collector so the analysis prompt and the zero-token property stay untouched. It arrived as a pull request that was reviewed, added as an example to our dataset, tested against our dataset to prevent regressions and prove the fix worked, and then merged.","We will continue to use SRE Agent internally and have begun rolling this out to some current LangSmith customers. We’re actively working to develop it and expand its capabilities for Kubernetes and other parts of the stack. Some of the next improvements will be making the state durable for HITL, and making the monitoring loop stateful so it has memory of the recent incidents reported. It is open source and available here. https://github.com/langchain-samples/sre-agent：https://github.com/langchain-samples/sre-agent . Feel free to try it out, contribute, or provide feedback.","LangSmith, our agent engineering platform, helps developers debug every agent decision, eval changes, and deploy in one click."],"articleImages":[],"mediaStatus":"none","articleBodyZh":["我们如何为 Kubernetes 部署构建一个自主 SRE 代理","我是 LangChain 的一名部署工程师，我工作的很大一部分都依赖于 Kubernetes。我维护我们内部的自建集群（也是新自建功能首先落地的地方，这样我们的团队可以在功能上线时进行测试），并帮助客户启动和升级他们自己的自建环境。做这些工作需要对活跃集群有足够深入的心理模型，以至于当我们的集群宕机时，我可以阅读架构，找出故障并修复它。在全职工作的基础上持续做这种事情是非常耗费精力的，而且这并不只是我一个人的经历。任何从事基础设施工作的人都知道这些痛苦。","所以我们构建了一个自主 SRE 代理，以减少排查时间和修复时间。目标是提高基础设施的可靠性并减轻团队的认知负担。我们希望它能够排查 Kubernetes 的健康状况、提出修复方案，并且仅在集群或基础设施需要更改时才引入人工干预。这篇文章涵盖了原因、我们如何构建它、为什么 LangSmith 让它值得信赖，以及我们看到的好处。","Kubernetes 发出大量信号（Pod 阶段、重启计数、HPA（水平 Pod 自动扩缩）状态、节点状态、警告事件、部署就绪状态，覆盖数十个命名空间），几乎没有任何综合信息。值班工程师使用这些信号来回答三个问题：现在是否有故障（崩溃循环、OOM 杀死、零就绪端点）；是否有即将发生的故障（HPA 锁定在最大值、单副本服务、:latest 镜像标签）；我们应该如何处理？有效回答这些问题需要判断能力，因此责任通常落在基础设施工程师或主题专家上，但90%的工作是机械性的排查，大部分情况下结果都是良好的。这种重复繁琐的工作容易让人精疲力竭，也会训练他们无意中忽略重要警报。","主动监控。调度器每N分钟检查一次健康状况，而无需唤醒完整代理。它通过 Kubernetes Python 客户端收集原始集群状态（不消耗任何 LLM 令牌），然后通过强制使用工具的一次 Claude Haiku 调用生成结构化健康报告，并按严重性排序发送到 Slack。","按需调查。当需要诊断问题时，协调器会并行扩展到专门的子代理：pod-inspector、scaling-analyzer、performance-analyzer、log-analyzer、security-auditor、reliability-auditor 等。每个子代理在综合一个优先报告之前会独立读取集群信息。","代理可以读取整个集群，但自身不会更改任何内容。每一次写操作（扩展部署、重启滚动更新、修补 HPA）都存在于单独的 change-executor 子代理中，并且每个写工具都由人工干预（HITL）控制。代理提出修复方案，由人工直接在 Slack 消息中批准、拒绝或编辑。读取是自主的，而写入始终通过 HITL 控制执行。这在结构上得到强制执行，并由集群内的 RBAC 反映（集群范围内读取，写入权限严格限定）。","下面的每一个选择都是一个分岔点，在明显路径与正确路径之间分开。","我们架构中的主线是尽量保持事情简单且高效。我们只在能改善我们所管理基础设施的代理效果的地方消耗代币、模型算力并增加网络表面。这使得代理足够便宜，可以每隔几分钟运行一次，并足够安全，可指向生产环境。","第 2 部分的每一个决策都以 LangSmith 跟踪运行：计划的 Haiku 检查、每个子代理调查、每一次读取和提出的写入。Deep Agents 和 LangGraph 部分自动跟踪；调度器的直接 Anthropic 调用使用 @traceable 包装。","这些标记的运行构成了我们评估的骨架。被错误分类的 pod 或错过的 OOM 会被提升为 LangSmith 数据集并附上正确答案，然后每条提示或模型调整都会在其上运行，使用 LLM 作为评判者和基于代码的评估器，这样回退的改动会显示为红色数字且不会合并。以上单副本的误报会成为我们评估套件的永久测试用例。","上面的改进循环仍然依赖于人类发现不良行为、找到追踪记录并将其提升为数据集。这对维护代理的人来说是耗时的工作。LangSmith 引擎：https://docs.langchain.com/langsmith/engine-overview 可以自动完成这项手动工作。它就像一个主动的代理工程师，在三个阶段监视我们的追踪项目。","引擎已经将我们的追踪记录分组为我们自己尚未提交的未解决问题。一个例子是，计划的健康检查根本没有收集任何利用率数据。收集器查询了节点、Pods、警告事件、HPAs 和部署，而分析步骤只是一次单一的强制工具调用，而没有可用工具，因此模型无法获取收集器跳过的内容。每小时报告都会提出一个结构上无法回答的容量问题，并以“检查 Pod 的 CPU/内存指标”之类的建议操作交回给我们。kubectl_top_pods 和 kubectl_top_nodes 功能已经在交互式代理的代码库中，但从未接入计划路径。引擎提出将 Pod 和节点指标接入收集器，重用现有的单位解析，而不是发明新的逻辑，并将变更范围限定在收集器中，以便分析提示和零令牌属性保持不变。它以拉取请求的形式到来，经过审核，添加为我们数据集的示例，针对我们的数据集进行测试以防止回归并验证修复有效，然后合并。","我们将继续在内部使用 SRE Agent，并已开始向一些现有的 LangSmith 客户推广。我们正在积极开发它，并扩展它在 Kubernetes 和栈的其他部分的能力。下一步的一些改进将是使状态对于 HITL 耐久化，并使监控循环具有状态性，以便它记忆最近报告的事件。它是开源的，可在此获取：https://github.com/langchain-samples/sre-agent：https://github.com/langchain-samples/sre-agent。欢迎尝试、贡献或提供反馈。","LangSmith，我们的代理工程平台，帮助开发者调试每一个代理决策、评估变更，并一键部署。"],"translationStatus":"translated","bodyOrigin":"source-page","editorial":{"summary":"Aioga 编辑摘要：LangChain 基于 Deep Agents 为 Kubernetes 部署构建了自主 SRE 智能体，可自动执行运维任务，并对变更操作引入人工审批机制。 Aioga 将其归入「技巧观点」方向，重点关注它对真实使用和行业竞争的影响。","background":"背景分析：产品与工具类动态的价值取决于它是否解决明确场景、能否进入工作流，以及交付、价格和数据安全是否可接受。","viewpoint":"Aioga 判断：这条动态更适合作为行业观察信号，当前信息足以建立线索，但不足以推导长期结论。","implications":"影响分析：对相关团队而言，短期应先核对来源、可用范围和实际成本，再判断是否值得接入或跟进。","nextStep":"后续观察：继续观察产品是否开放使用、用户反馈、定价、集成能力和后续版本更新。","evidenceRefs":["title","summary","articleBody"],"confidence":"medium","status":"published","aiGenerated":false,"autoApproved":true,"generatedBy":"rule-safe-fallback","generatedAt":"2026-08-11T09:23:27.661Z","sourceHash":"5089ecd88ccdb0da","validation":{"passed":true,"mode":"rule-safe-fallback","checks":["schema","length","source-attribution","no-html"]}},"tags":["技巧观点","LangChain：Blog（RSS）"],"translations":{"zh-CN":{"title":"LangChain 如何为 Kubernetes 构建自主 SRE 智能体","summary":"LangChain 基于 Deep Agents 为 Kubernetes 部署构建了自主 SRE 智能体，可自动执行运维任务，并对变更操作引入人工审批机制。该智能体使用 LangSmith 进行全链路追踪，并通过 evals 评估系统性能，兼顾自动化效率与运维安全。","category":"技巧观点","source":"langchain.com","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"LangChain 如何为 Kubernetes 构建自主 SRE 智能体 - Aioga AI资讯","description":"LangChain 基于 Deep Agents 为 Kubernetes 部署构建了自主 SRE 智能体，可自动执行运维任务，并对变更操作引入人工审批机制。该智能体使用 LangSmith 进行全链路追踪，并通过 evals 评估系统性能，兼顾自动化效率与运维安全。","url":"https://www.aioga.com/news/cmsgb0jc7028uro3pnxg6blix/","articleBody":["我们如何为 Kubernetes 部署构建一个自主 SRE 代理","我是 LangChain 的一名部署工程师，我工作的很大一部分都依赖于 Kubernetes。我维护我们内部的自建集群（也是新自建功能首先落地的地方，这样我们的团队可以在功能上线时进行测试），并帮助客户启动和升级他们自己的自建环境。做这些工作需要对活跃集群有足够深入的心理模型，以至于当我们的集群宕机时，我可以阅读架构，找出故障并修复它。在全职工作的基础上持续做这种事情是非常耗费精力的，而且这并不只是我一个人的经历。任何从事基础设施工作的人都知道这些痛苦。","所以我们构建了一个自主 SRE 代理，以减少排查时间和修复时间。目标是提高基础设施的可靠性并减轻团队的认知负担。我们希望它能够排查 Kubernetes 的健康状况、提出修复方案，并且仅在集群或基础设施需要更改时才引入人工干预。这篇文章涵盖了原因、我们如何构建它、为什么 LangSmith 让它值得信赖，以及我们看到的好处。","Kubernetes 发出大量信号（Pod 阶段、重启计数、HPA（水平 Pod 自动扩缩）状态、节点状态、警告事件、部署就绪状态，覆盖数十个命名空间），几乎没有任何综合信息。值班工程师使用这些信号来回答三个问题：现在是否有故障（崩溃循环、OOM 杀死、零就绪端点）；是否有即将发生的故障（HPA 锁定在最大值、单副本服务、:latest 镜像标签）；我们应该如何处理？有效回答这些问题需要判断能力，因此责任通常落在基础设施工程师或主题专家上，但90%的工作是机械性的排查，大部分情况下结果都是良好的。这种重复繁琐的工作容易让人精疲力竭，也会训练他们无意中忽略重要警报。","主动监控。调度器每N分钟检查一次健康状况，而无需唤醒完整代理。它通过 Kubernetes Python 客户端收集原始集群状态（不消耗任何 LLM 令牌），然后通过强制使用工具的一次 Claude Haiku 调用生成结构化健康报告，并按严重性排序发送到 Slack。","按需调查。当需要诊断问题时，协调器会并行扩展到专门的子代理：pod-inspector、scaling-analyzer、performance-analyzer、log-analyzer、security-auditor、reliability-auditor 等。每个子代理在综合一个优先报告之前会独立读取集群信息。","代理可以读取整个集群，但自身不会更改任何内容。每一次写操作（扩展部署、重启滚动更新、修补 HPA）都存在于单独的 change-executor 子代理中，并且每个写工具都由人工干预（HITL）控制。代理提出修复方案，由人工直接在 Slack 消息中批准、拒绝或编辑。读取是自主的，而写入始终通过 HITL 控制执行。这在结构上得到强制执行，并由集群内的 RBAC 反映（集群范围内读取，写入权限严格限定）。","下面的每一个选择都是一个分岔点，在明显路径与正确路径之间分开。","我们架构中的主线是尽量保持事情简单且高效。我们只在能改善我们所管理基础设施的代理效果的地方消耗代币、模型算力并增加网络表面。这使得代理足够便宜，可以每隔几分钟运行一次，并足够安全，可指向生产环境。","第 2 部分的每一个决策都以 LangSmith 跟踪运行：计划的 Haiku 检查、每个子代理调查、每一次读取和提出的写入。Deep Agents 和 LangGraph 部分自动跟踪；调度器的直接 Anthropic 调用使用 @traceable 包装。","这些标记的运行构成了我们评估的骨架。被错误分类的 pod 或错过的 OOM 会被提升为 LangSmith 数据集并附上正确答案，然后每条提示或模型调整都会在其上运行，使用 LLM 作为评判者和基于代码的评估器，这样回退的改动会显示为红色数字且不会合并。以上单副本的误报会成为我们评估套件的永久测试用例。","上面的改进循环仍然依赖于人类发现不良行为、找到追踪记录并将其提升为数据集。这对维护代理的人来说是耗时的工作。LangSmith 引擎：https://docs.langchain.com/langsmith/engine-overview 可以自动完成这项手动工作。它就像一个主动的代理工程师，在三个阶段监视我们的追踪项目。","引擎已经将我们的追踪记录分组为我们自己尚未提交的未解决问题。一个例子是，计划的健康检查根本没有收集任何利用率数据。收集器查询了节点、Pods、警告事件、HPAs 和部署，而分析步骤只是一次单一的强制工具调用，而没有可用工具，因此模型无法获取收集器跳过的内容。每小时报告都会提出一个结构上无法回答的容量问题，并以“检查 Pod 的 CPU/内存指标”之类的建议操作交回给我们。kubectl_top_pods 和 kubectl_top_nodes 功能已经在交互式代理的代码库中，但从未接入计划路径。引擎提出将 Pod 和节点指标接入收集器，重用现有的单位解析，而不是发明新的逻辑，并将变更范围限定在收集器中，以便分析提示和零令牌属性保持不变。它以拉取请求的形式到来，经过审核，添加为我们数据集的示例，针对我们的数据集进行测试以防止回归并验证修复有效，然后合并。","我们将继续在内部使用 SRE Agent，并已开始向一些现有的 LangSmith 客户推广。我们正在积极开发它，并扩展它在 Kubernetes 和栈的其他部分的能力。下一步的一些改进将是使状态对于 HITL 耐久化，并使监控循环具有状态性，以便它记忆最近报告的事件。它是开源的，可在此获取：https://github.com/langchain-samples/sre-agent：https://github.com/langchain-samples/sre-agent。欢迎尝试、贡献或提供反馈。","LangSmith，我们的代理工程平台，帮助开发者调试每一个代理决策、评估变更，并一键部署。"]},"en":{"title":"How LangChain Builds Autonomous SRE Agents for Kubernetes","summary":"LangChain has built autonomous SRE agents for Kubernetes deployments based on Deep Agents, capable of automatically performing operational tasks and introducing a human approval mechanism for change operations. These agents use LangSmith for full-chain tracing and evaluate system performance through evals, balancing automation efficiency with operational safety.","category":"Insights","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"How LangChain Builds Autonomous SRE Agents for Kubernetes - Aioga AI News","description":"LangChain has built autonomous SRE agents for Kubernetes deployments based on Deep Agents, capable of automatically performing operational tasks and introducing a human approval me...","url":"https://www.aioga.com/en/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:13.170Z"},"ja":{"title":"LangChain は Kubernetes のための自律 SRE エージェントをどのように構築するか","summary":"LangChain は Deep Agents を基に、Kubernetes デプロイのための自律 SRE エージェントを構築しました。このエージェントは運用タスクを自動で実行可能で、変更操作には人工承認メカニズムを導入しています。エージェントは LangSmith を使用して全チェーンの追跡を行い、evals を通じてシステム性能を評価し、自動化効率と運用の安全性を両立させます。","category":"ヒントと視点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"LangChain は Kubernetes のための自律 SRE エージェントをどのように構築するか - Aioga AIニュース","description":"LangChain は Deep Agents を基に、Kubernetes デプロイのための自律 SRE エージェントを構築しました。このエージェントは運用タスクを自動で実行可能で、変更操作には人工承認メカニズムを導入しています。エージェントは LangSmith を使用して全チェーンの追跡を行い、evals を通じてシステム性能を評価し、自動化効率と運用...","url":"https://www.aioga.com/ja/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:15.581Z"},"ko":{"title":"LangChain이 Kubernetes용 자율 SRE 에이전트를 구축하는 방법","summary":"LangChain은 Deep Agents 기반의 자율 SRE 배포 에이전트를 구축했으며, 운영 및 유지보수 작업을 자동으로 실행할 수 있고, 변경 작업을 위한 수동 승인 메커니즘을 도입했습니다. 이 에이전트는 LangSmith를 사용하여 전체 링크 추적을 수행하고, 시스템 성능을 평가하여 자동화 효율성과 운영 보안을 균형 있게 평가합니다.","category":"인사이트","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"LangChain이 Kubernetes용 자율 SRE 에이전트를 구축하는 방법 - Aioga AI 뉴스","description":"LangChain은 Deep Agents 기반의 자율 SRE 배포 에이전트를 구축했으며, 운영 및 유지보수 작업을 자동으로 실행할 수 있고, 변경 작업을 위한 수동 승인 메커니즘을 도입했습니다. 이 에이전트는 LangSmith를 사용하여 전체 링크 추적을 수행하고, 시스템 성능을 평가하여 자동화 효율성과 운영 보안을 균형...","url":"https://www.aioga.com/ko/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:24.126Z"},"es":{"title":"Cómo construye LangChain agentes SRE autónomos para Kubernetes","summary":"LangChain, basado en Deep Agents, ha construido agentes SRE autónomos para despliegues en Kubernetes, que pueden ejecutar tareas de operaciones automáticamente e introducir un mecanismo de aprobación humana para operaciones de cambio. Este agente utiliza LangSmith para el seguimiento de toda la cadena y evalúa el rendimiento del sistema mediante evals, equilibrando eficiencia automatizada y seguridad operativa.","category":"Ideas","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"Cómo construye LangChain agentes SRE autónomos para Kubernetes - Aioga Noticias de IA","description":"LangChain, basado en Deep Agents, ha construido agentes SRE autónomos para despliegues en Kubernetes, que pueden ejecutar tareas de operaciones automáticamente e introducir un meca...","url":"https://www.aioga.com/es/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:20.775Z"},"fr":{"title":"Comment LangChain construit un agent SRE autonome pour Kubernetes","summary":"LangChain, basé sur Deep Agents, a construit un agent SRE autonome pour le déploiement Kubernetes, capable d'exécuter automatiquement des tâches d'exploitation et de maintenance, et d'introduire un mécanisme d'approbation humaine pour les opérations de modification. Cet agent utilise LangSmith pour le suivi complet de la chaîne et évalue les performances du système via evals, conciliant efficacité automatisée et sécurité opérationnelle.","category":"Analyses","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"Comment LangChain construit un agent SRE autonome pour Kubernetes - Aioga Actualités IA","description":"LangChain, basé sur Deep Agents, a construit un agent SRE autonome pour le déploiement Kubernetes, capable d'exécuter automatiquement des tâches d'exploitation et de maintenance, e...","url":"https://www.aioga.com/fr/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:31.647Z"},"de":{"title":"Wie LangChain autonome SRE-Agenten für Kubernetes erstellt","summary":"LangChain hat auf Basis von Deep Agents autonome SRE-Agenten für Kubernetes-Deployments entwickelt, die Wartungsaufgaben automatisch ausführen und einen menschlichen Genehmigungsmechanismus für Änderungsoperationen einführen. Dieser Agent verwendet LangSmith für die vollständige Nachverfolgung und evals zur Bewertung der Systemleistung, wobei sowohl Automatisierungseffizienz als auch Wartungssicherheit berücksichtigt werden.","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"Wie LangChain autonome SRE-Agenten für Kubernetes erstellt - Aioga KI-News","description":"LangChain hat auf Basis von Deep Agents autonome SRE-Agenten für Kubernetes-Deployments entwickelt, die Wartungsaufgaben automatisch ausführen und einen menschlichen Genehmigungsme...","url":"https://www.aioga.com/de/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:30.738Z"},"pt-BR":{"title":"Como LangChain constrói agentes SRE autônomos para Kubernetes","summary":"LangChain, com base em Deep Agents, construiu agentes SRE autônomos para implantações em Kubernetes, capazes de executar tarefas de operação e manutenção automaticamente e introduzir mecanismos de aprovação manual para operações de mudança. Esses agentes utilizam LangSmith para rastreamento de ponta a ponta e avaliam o desempenho do sistema por meio de evals, equilibrando eficiência automatizada e segurança operacional.","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"Como LangChain constrói agentes SRE autônomos para Kubernetes - Aioga Notícias de IA","description":"LangChain, com base em Deep Agents, construiu agentes SRE autônomos para implantações em Kubernetes, capazes de executar tarefas de operação e manutenção automaticamente e introduz...","url":"https://www.aioga.com/pt-BR/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:37.655Z"},"ru":{"title":"Как LangChain создает автономного SRE-агента для Kubernetes","summary":"LangChain на базе Deep Agents создал автономного SRE-агента для развертывания Kubernetes, который может автоматически выполнять задачи эксплуатации и вводит механизм ручного утверждения для операций изменений. Этот агент использует LangSmith для полного отслеживания цепочки и через evals оценивает производительность системы, сочетая автоматизацию с безопасностью эксплуатации.","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"Как LangChain создает автономного SRE-агента для Kubernetes - Aioga Новости ИИ","description":"LangChain на базе Deep Agents создал автономного SRE-агента для развертывания Kubernetes, который может автоматически выполнять задачи эксплуатации и вводит механизм ручного утверж...","url":"https://www.aioga.com/ru/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:37.314Z"},"ar":{"title":"كيف تقوم LangChain ببناء وكيل ذكي ذاتي لـ SRE من أجل Kubernetes","summary":"بنية LangChain لوكيل SRE ذكي ذاتي على Kubernetes تعتمد على Deep Agents، يمكنه تنفيذ مهام الصيانة تلقائيًا، وإدخال آلية الموافقة البشرية على تغييرات العمليات. يستخدم هذا الوكيل LangSmith لتتبع كامل العملية، ويقوم بتقييم أداء النظام من خلال evals، مما يجمع بين كفاءة الأتمتة وأمان التشغيل.","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"كيف تقوم LangChain ببناء وكيل ذكي ذاتي لـ SRE من أجل Kubernetes - Aioga أخبار الذكاء الاصطناعي","description":"بنية LangChain لوكيل SRE ذكي ذاتي على Kubernetes تعتمد على Deep Agents، يمكنه تنفيذ مهام الصيانة تلقائيًا، وإدخال آلية الموافقة البشرية على تغييرات العمليات. يستخدم هذا الوكيل Lang...","url":"https://www.aioga.com/ar/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:43.938Z"},"hi":{"title":"LangChain कैसे Kubernetes के लिए स्वायत्त SRE एजेेंट बनाता है","summary":"LangChain ने Deep Agents के आधार पर Kubernetes परिनियोजन के लिए स्वायत्त SRE एजेेंट बनाया है, जो स्वचालित रूप से संचालन कार्यों को निष्पादित कर सकता है और परिवर्तन संचालन में मानव अनुमोदन तंत्र को शामिल करता है। यह एजेेंट LangSmith का उपयोग करके पूरी श्रृंखला का ट्रैक करता है और evals के माध्यम से सिस्टम प्रदर्शन का मूल्यांकन करता है, जिससे स्वचालन दक्षता और संचालन सुरक्षा दोनों सुनिश्चित होती हैं।","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"LangChain कैसे Kubernetes के लिए स्वायत्त SRE एजेेंट बनाता है - Aioga AI समाचार","description":"LangChain ने Deep Agents के आधार पर Kubernetes परिनियोजन के लिए स्वायत्त SRE एजेेंट बनाया है, जो स्वचालित रूप से संचालन कार्यों को निष्पादित कर सकता है और परिवर्तन संचालन में मानव...","url":"https://www.aioga.com/hi/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:44.426Z"},"it":{"title":"Come LangChain costruisce un agente SRE autonomo per Kubernetes","summary":"LangChain ha costruito un agente SRE autonomo per il deployment su Kubernetes basato su Deep Agents, in grado di eseguire automaticamente compiti operativi e di introdurre un meccanismo di approvazione manuale per le modifiche operative. L'agente utilizza LangSmith per il tracciamento end-to-end e valuta le prestazioni del sistema tramite evals, bilanciando efficienza automatizzata e sicurezza operativa.","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"Come LangChain costruisce un agente SRE autonomo per Kubernetes - Aioga Notizie IA","description":"LangChain ha costruito un agente SRE autonomo per il deployment su Kubernetes basato su Deep Agents, in grado di eseguire automaticamente compiti operativi e di introdurre un mecca...","url":"https://www.aioga.com/it/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:51.334Z"},"nl":{"title":"Hoe LangChain autonome SRE-agents bouwt voor Kubernetes","summary":"LangChain heeft op basis van Deep Agents autonome SRE-agents ontwikkeld voor Kubernetes-implementaties, die automatisch operationele taken uitvoeren en een handmatige goedkeuringsmechanisme voor wijzigingsbewerkingen introduceren. Deze agent gebruikt LangSmith voor end-to-end tracking en evalueert systeemprestaties via evals, waardoor zowel automatiseringsefficiëntie als operationele veiligheid wordt gewaarborgd.","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"Hoe LangChain autonome SRE-agents bouwt voor Kubernetes - Aioga AI-nieuws","description":"LangChain heeft op basis van Deep Agents autonome SRE-agents ontwikkeld voor Kubernetes-implementaties, die automatisch operationele taken uitvoeren en een handmatige goedkeuringsm...","url":"https://www.aioga.com/nl/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:49.708Z"},"tr":{"title":"LangChain Kubernetes için Otonom SRE Ajanı Nasıl İnşa Eder","summary":"LangChain, Deep Agents temelinde Kubernetes dağıtımı için otonom SRE ajanı inşa etti; bu ajan bakım görevlerini otomatik olarak gerçekleştirebilir ve değişiklik işlemlerine insan onayı mekanizması ekler. Bu ajan, tam zincir izleme için LangSmith kullanır ve sistem performansını değerlendirmek için evals ile analiz ederek otomasyon verimliliği ile bakım güvenliğini dengeler.","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"LangChain Kubernetes için Otonom SRE Ajanı Nasıl İnşa Eder - Aioga AI Haberleri","description":"LangChain, Deep Agents temelinde Kubernetes dağıtımı için otonom SRE ajanı inşa etti; bu ajan bakım görevlerini otomatik olarak gerçekleştirebilir ve değişiklik işlemlerine insan o...","url":"https://www.aioga.com/tr/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:56.806Z"},"vi":{"title":"LangChain cách xây dựng tác nhân SRE tự chủ cho Kubernetes","summary":"LangChain dựa trên Deep Agents đã xây dựng tác nhân SRE tự chủ cho việc triển khai Kubernetes, có thể tự động thực hiện các nhiệm vụ vận hành và đưa cơ chế phê duyệt thủ công vào các thao tác thay đổi. Tác nhân này sử dụng LangSmith để theo dõi toàn bộ chuỗi và đánh giá hiệu suất hệ thống thông qua evals, cân bằng giữa hiệu quả tự động hóa và an toàn vận hành.","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"LangChain cách xây dựng tác nhân SRE tự chủ cho Kubernetes - Tin tức AI Aioga","description":"LangChain dựa trên Deep Agents đã xây dựng tác nhân SRE tự chủ cho việc triển khai Kubernetes, có thể tự động thực hiện các nhiệm vụ vận hành và đưa cơ chế phê duyệt thủ công vào c...","url":"https://www.aioga.com/vi/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:43:57.397Z"},"id":{"title":"LangChain Bagaimana Membangun Agen SRE Otonom untuk Kubernetes","summary":"LangChain, berdasarkan Deep Agents, membangun agen SRE otonom untuk penerapan Kubernetes, yang dapat secara otomatis menjalankan tugas operasional dan memperkenalkan mekanisme persetujuan manusia untuk operasi perubahan. Agen ini menggunakan LangSmith untuk pelacakan rantai penuh dan mengevaluasi kinerja sistem melalui evals, menyeimbangkan efisiensi otomatisasi dengan keamanan operasional.","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"LangChain Bagaimana Membangun Agen SRE Otonom untuk Kubernetes - Berita AI Aioga","description":"LangChain, berdasarkan Deep Agents, membangun agen SRE otonom untuk penerapan Kubernetes, yang dapat secara otomatis menjalankan tugas operasional dan memperkenalkan mekanisme pers...","url":"https://www.aioga.com/id/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:44:03.389Z"},"th":{"title":"LangChain สร้างเอเย่นต์ SRE อัตโนมัติสำหรับ Kubernetes ได้อย่างไร","summary":"LangChain สร้างเอเย่นต์ SRE อัตโนมัติสำหรับการปรับใช้ Kubernetes โดยใช้ Deep Agents ซึ่งสามารถดำเนินงานดูแลรักษาแบบอัตโนมัติ และมีการเพิ่มกลไกการอนุมัติด้วยมือสำหรับการดำเนินการเปลี่ยนแปลง เอเย่นต์นี้ใช้ LangSmith เพื่อติดตามโซ่กระบวนการทั้งหมด และประเมินประสิทธิภาพของระบบผ่าน evals ควบคู่ไปกับประสิทธิภาพการอัตโนมัติและความปลอดภัยในการปฏิบัติการ","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"LangChain สร้างเอเย่นต์ SRE อัตโนมัติสำหรับ Kubernetes ได้อย่างไร - ข่าว AI Aioga","description":"LangChain สร้างเอเย่นต์ SRE อัตโนมัติสำหรับการปรับใช้ Kubernetes โดยใช้ Deep Agents ซึ่งสามารถดำเนินงานดูแลรักษาแบบอัตโนมัติ และมีการเพิ่มกลไกการอนุมัติด้วยมือสำหรับการดำเนินการเปล...","url":"https://www.aioga.com/th/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:44:04.124Z"},"pl":{"title":"Jak LangChain buduje autonomicznego agenta SRE dla Kubernetes","summary":"LangChain, opierając się na Deep Agents, stworzył autonomicznego agenta SRE dla wdrożeń Kubernetes, który może automatycznie wykonywać zadania operacyjne i wprowadza mechanizm ręcznej akceptacji dla operacji zmiany. Agent korzysta z LangSmith do pełnej ścieżki śledzenia i oceny wydajności systemu za pomocą evals, łącząc wydajność automatyzacji z bezpieczeństwem operacji.","category":"技巧观点","source":"LangChain：Blog（RSS）","aggregationSource":"LangChain：Blog（RSS）","pageTitle":"Jak LangChain buduje autonomicznego agenta SRE dla Kubernetes - Aioga Wiadomości AI","description":"LangChain, opierając się na Deep Agents, stworzył autonomicznego agenta SRE dla wdrożeń Kubernetes, który może automatycznie wykonywać zadania operacyjne i wprowadza mechanizm ręcz...","url":"https://www.aioga.com/pl/news/cmsgb0jc7028uro3pnxg6blix/","contentTranslated":true,"sourceHash":"3c78a0c7998034f7","translatedAt":"2026-08-05T16:44:11.254Z"}}}}