Agentic-Ops Control Plane

Name: Agentic-Ops Control Plane
Author: nolll77

作者：nolll77版本 0.1.14i★ –i电脑或云端

注册名称io.github.nolll77/agentic-ops

安装

概览

作者提供的说明（英文原文）

Autonomous and governed SRE & MLOps control plane for AI agent platforms.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 电脑或云端
传输方式: stdio, Streamable HTTP
分类: 其他

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在作者的服务器上运行：你向它提出的内容会经过他们的服务器。
注册中心没有列出需要填写的密钥。

你需要

在云端运行无需安装任何东西，通过互联网连接
PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

适用于网页版、桌面版和手机版 Claude：

打开 Settings → Connectors
点击 Add custom connector
粘贴下面的地址，然后点击 Add
如果要求登录，请登录作者的服务

https://agentic-ops-5ebv.onrender.com/mcp

或者在你的电脑上运行

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "agentic-ops": {
      "command": "uvx",
      "args": [
        "agentic-ops"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add --transport http agentic-ops https://agentic-ops-5ebv.onrender.com/mcp

或者在你的电脑上运行

claude mcp add agentic-ops -- uvx agentic-ops

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "agentic-ops": {
      "url": "https://agentic-ops-5ebv.onrender.com/mcp"
    }
  }
}

或者在你的电脑上运行

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "agentic-ops": {
      "command": "uvx",
      "args": [
        "agentic-ops"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "agentic-ops": {
      "type": "http",
      "url": "https://agentic-ops-5ebv.onrender.com/mcp"
    }
  }
}

或者在你的电脑上运行

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "agentic-ops": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "agentic-ops"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

ChatGPT 只能连接互联网上的服务器：

打开网页版 ChatGPT → Settings → Connectors (Apps)
点击 Create 或 Add（部分套餐需要先在 Advanced 中开启 Developer mode）
粘贴下面的地址，起个名字，然后保存
如果要求登录，请登录作者的服务

https://agentic-ops-5ebv.onrender.com/mcp

菜单名称可能因套餐和版本不同而略有差异。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

我们还没有读取这个服务器的工具列表；我们会轮流检查各个端点。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 nolll77 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
… 尚未检查
端点有响应
尚未检查；我们会轮流检查各个端点
… 尚未检查
使用 OAuth 登录
尚未检查；我们会轮流检查各个端点
… 尚未检查
工具描述中没有隐藏指令
尚未检查；我们会在检查端点时读取工具列表

与“其他”中的其他服务器比较i

星标数多于此分类中其他 32589 个服务器的 0%
发布到注册中心的时间比此分类中其他 32589 个服务器的 69% 更新
基础检查结果等于或优于此分类中其他 32589 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Agentic-Ops Control Plane 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

两种方式都可以：安装在你的电脑上，或者连接云端版本。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.1.14）于17天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接，所以接受远程 MCP 服务器的应用也可以使用，例如开启开发者模式的 ChatGPT。

有什么可以替代它？

同一分类中有：Spec Driven Development, Remote Desktop Commander, Detailing Manager

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者：formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新

安装

Remote Desktop Commander

作者：desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新

安装

Detailing Manager

作者：RadoslavMoshev

Connect your auto detailing studio's tasks, cars, clients and revenue to Claude or ChatGPT.

在你的电脑上运行今天更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Agentic-Ops Control Plane

-059669)

Language / Langue : English (Main Documentation) | [Lire la documentation complète en Français (README_FR.md)](README_FR.md)

Official Anthropic Model Context Protocol Registry: io.github.nolll77/agentic-ops (v0.1.12 Active) | Glama MCP Catalog: glama.ai/mcp/servers/nolll77/agentic-ops

mcp-name: io.github.nolll77/agentic-ops

Agentic-Ops is a personal MLOps control-plane project for exploring how AI agents (LLMs) can help investigate operational signals such as Data Drift, Model Staleness, and OOMKilled Pods.

Unlike frameworks that prioritize agent autonomy, it explores SRE-inspired controls: deterministic policies, observability, fail-closed authorization, asynchronous human review, and cryptographic auditability. These features are engineering work in progress, not a claim of production qualification or EU AI Act compliance.

Current release and honest qualification status

The Python package is distributed on PyPI; multi-architecture container images (linux/amd64, linux/arm64) are distributed on GHCR (ghcr.io/nolll77/agentic-ops) and Docker Hub (nolll78/agentic-ops).
A local Docker/kind exercise validated an alert webhook through the worker to a simulated investigation episode, and confirmed that its state survived a pod restart on the same PVC. This is local single-node evidence, not a backup-restore or cluster-loss test.
Release automation tests and version-checks a GitHub release, publishes Python distributions to PyPI, then builds and publishes the matching AMD64/ARM64 container to both GHCR and Docker Hub. See docs/CONTAINER_IMAGE_PUBLICATION.md.
Release v0.1.4 adds a fail-closed local inventory, SQLite backup/restore commands, and a synthetic load/crash-recovery benchmark. These tools do not themselves qualify a production deployment.
Release v0.1.5 improves protected console/LLM-probe authentication UX. Render must still have a private ADMIN_API_KEY configured for these protected actions.
Release v0.1.6 adds verified multi-database/evidence backup sets, a simulation-only approval continuation on the original episode, expanded worker crash and inventory lifecycle tests, and a size-matrix benchmark. It does not certify an off-host restore or enable live actions.
Production qualification is not claimed. Entra/SCIM, multi-node HA, deployment-target backup restoration, recovery objectives, synchronization of local inventory with a live source, and action-by-action live execution remain unqualified.
The detailed repository-to-production mapping, missing target-specific values, owners, validation evidence, and promotion gates are documented in docs/INDUSTRIAL_PRODUCTION_BLUEPRINT.md.
Render console/chat probe setup and the exact ADMIN_API_KEY prerequisite are documented in docs/RENDER_CONSOLE_CHAT_TROUBLESHOOTING.md.
A solo, single-node observation/simulation profile does not require an Entra tenant. Its scope and remaining self-operated qualification work are in docs/SELF_HOSTED_PRODUCTION_PATH.md. This is distinct from enterprise SSO, HA and independent audit anchoring.

For implemented behavior versus open production gates, see docs/INDUSTRIAL_IMPLEMENTATION_STATUS.md and docs/PRODUCTION_READINESS_ROADMAP_AND_GAPS.md.

Project Origin: Extracted from `nolll77/car_accident` into an MLOps Control Plane

Originally, Agentic-Ops was developed as an SRE governance and self-healing component inside the road-safety MLOps platform [`nolll77/car_accident`](https://github.com/nolll77/car_accident) (BAAC data warehouse, Feast feature store, MLflow, and FastAPI inference).

It was extracted into this standalone repository to explore a reusable MLOps control plane:

Zero Domain Coupling: All dataset-specific logic (car_accident) was removed in favor of a Hexagonal Architecture (`Ports & Adapters`) driven by declarative YAML policies (autonomy_policy.yaml, action_catalog.yaml, risk_register.yaml).
Designed for adapters: The core engine (Orchestrator FSM, PolicyEngine, Trust Ledger) uses canonical Signal and SystemFacts contracts. Connecting it to another system still requires a suitable adapter, security review, and qualification for that target.

Live Demonstration & Validation Reports

The following public Render instance and reports demonstrate selected features. They are not evidence of production qualification or an availability guarantee:

Live Serverless Auto-Wake Production Instance (`Render` + `Tailscale` + `cron-job.org`):

Live Control Plane Status: [`https://agentic-ops-5ebv.onrender.com/`](https://agentic-ops-5ebv.onrender.com/)
Live Interactive SRE Web Console (`/console`): [`https://agentic-ops-5ebv.onrender.com/console`](https://agentic-ops-5ebv.onrender.com/console)
💬 Live Interactive Web Chat Assistant (`/chat`): [`https://agentic-ops-5ebv.onrender.com/chat`](https://agentic-ops-5ebv.onrender.com/chat)
Live `MCP` (`Model Context Protocol`) Manifest (`/mcp/manifest`): [`https://agentic-ops-5ebv.onrender.com/mcp/manifest`](https://agentic-ops-5ebv.onrender.com/mcp/manifest) *(Full guide: [`docs/MCP_SERVER_GUIDE.md`](docs/MCP_SERVER_GUIDE.md))*
Live Tailscale Mesh Status (`100.85.*.*` anonymized): [`https://agentic-ops-5ebv.onrender.com/api/tailscale/status`](https://agentic-ops-5ebv.onrender.com/api/tailscale/status)
Live Read-Only Pull Snapshot (`jakatt/cac_mlops`): [`https://agentic-ops-5ebv.onrender.com/api/cac/snapshot`](https://agentic-ops-5ebv.onrender.com/api/cac/snapshot)

[!TIP]

### 💬 Try the Live SRE Assistant Web Chat (/chat)

Direct Access: [`https://agentic-ops-5ebv.onrender.com/chat`](https://agentic-ops-5ebv.onrender.com/chat)

Ask natural language SRE questions, explore the 435 Failure Modes Taxonomy, simulate the 36 incident scenarios, and inspect the Multi-Agent System (MAS) in real-time with zero tokens consumed.

Cloud Sandbox Validation Report (`Docker Agentic Platform`): End-to-end deployment on an isolated micro-VM and line-by-line technical interpretation of all 30 unit, adversarial, and cryptographic security tests: [Read the Cloud Sandbox Deployment & Validation Report (`DOCKER_SANDBOX_VALIDATION.md`)](DOCKER_SANDBOX_VALIDATION.md)
Tailscale Pull Interconnection Report with Target Platform [`jakatt/cac_mlops`](https://github.com/jakatt/cac_mlops): Userspace Tailscale tunneling (--tun=userspace-networking / SOCKS5 proxy / DERP relays), strict read-only ingestion across 5 observability sources (Loki, Prometheus, MLflow, Prefect, Evidently), and validation via 13 unit tests: [Read the Tailscale Pull Connector Architecture & Validation Report (`CAC_TAILSCALE_PULL_CONNECTOR.md`)](CAC_TAILSCALE_PULL_CONNECTOR.md)
Target Outage & Scaleway/Tailscale Degradation Resilience Guide: Explains why the Control Plane never crashes if the supervised VPS goes down, detailing RemoteServiceStatus.REMOTE_TARGET_OFFLINE error absorption, Fail-Closed governance, autonomous local test execution, and 1-minute self-healing recovery: [Read the Target Outage & Resilience Guide (`docs/TARGET_OUTAGE_AND_RESILIENCE_GUIDE.md`)](docs/TARGET_OUTAGE_AND_RESILIENCE_GUIDE.md)
Official Thesis Defense Report (PDF, 40 Pages): Comprehensive SRE & MLOps thesis defense report on governance, multi-layer observability, deterministic FSM, and interconnection with jakatt/cac_mlops: [Read the Official Defense Report (`docs/Rapport_soutenance_Mar25_MLOps_Agentic_Car_Accident.pdf`)](docs/Rapport_soutenance_Mar25_MLOps_Agentic_Car_Accident.pdf)

Presentation Slides Explaining this Repository

Online Google Slides Presentation: [`https://docs.google.com/presentation/d/1fi5rEGww1CexT4OUcDj1d9W8YQhkL5GoXZwWDtCjLA0/edit?usp=sharing`](https://docs.google.com/presentation/d/1fi5rEGww1CexT4OUcDj1d9W8YQhkL5GoXZwWDtCjLA0/edit?usp=sharing)

Foundational Knowledge Engineering: The MLOps Failure Taxonomy & Incident War-Room Scenarios Registry

An AI Agent cannot safely diagnose or govern a production MLOps platform without a formal, structured ontology of how machine learning systems fail in the real world. Before writing a single line of orchestration code, Agentic-Ops was grounded in two foundational engineering registries that serve as the cognitive, observability, and decision-making reference frame for the entire repository:

Foundational Reference Document	Direct Link in Repository	Scale & Depth	Operational Role in `Agentic-Ops`

| 1. MLOps Failure Modes & Risk Taxonomy (`v2`) | [`docs/mlops_failure_modes_taxonomy.md`](docs/mlops_failure_modes_taxonomy.md) | 435 Failure Modes across 10 System Dimensions (636 lines / 143 KB) | The Observability & Risk Compass. Catalogs every failure class (Infrastructure, Data Pipelines, Feature Store, Training, Serving, Observability, CI/CD, SecOps, FinOps, Org) calibrated by Severity (`1-5`), Probability (`1-5`), Detectability (`1-5`), `RPN` (`Risk Priority Number` `/125`), primary `SLO` impact, detection sensor, automated mitigation, and human runbook. Compiled via [`agentic_ops/cli/compile_tax

只显示了开头部分；其余内容请在仓库中阅读

Agentic-Ops Control Plane

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

或者在你的电脑上运行

或者在你的电脑上运行

或者在你的电脑上运行

或者在你的电脑上运行

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“其他”中的其他服务器比较i

常见问题

替代选择

Spec Driven Development

Remote Desktop Commander

Detailing Manager

作者的 README

Agentic-Ops Control Plane

Current release and honest qualification status

Project Origin: Extracted from nolll77/car_accident into an MLOps Control Plane

Live Demonstration & Validation Reports

Presentation Slides Explaining this Repository

Foundational Knowledge Engineering: The MLOps Failure Taxonomy & Incident War-Room Scenarios Registry

Project Origin: Extracted from `nolll77/car_accident` into an MLOps Control Plane