DevOps AI Guidelines & Learning Path
A comprehensive learning path for DevOps engineers to master AI, from basics to AI Infrastructure Architect.
- Source repo
- VersusControl/devops-ai-guidelines
- Stars
- ★ 1.6k
- Last updated
- 25d ago
- License
- MIT
- Primary language
- Python
- FA score
- 25/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platform
- Cost
- Free, no paid service needed
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- DevOps engineers seeking to learn AI skills and plan career growth.
- Not a fit if
- Readers who want runnable code examples; the repo is theory-only markdown
- DevOps teams that do not use AWS or Golang
- Learners who need interactive online tutorials or hands-on labs
- Source review
- 25/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
This repository provides a systematic learning path for DevOps professionals to advance from AI fundamentals to becoming an AI Infrastructure Architect. It includes a complete 18-month roadmap divided into three phases, alongside detailed guides on building MCP servers with Golang and Kubernetes, developing AI Agents with Golang and LangChain, monitoring systems with an SRE Agent, and managing projects with AI. Additionally, it offers team-oriented AI implementation guidelines, interview preparation, AI prompts, AWS certification acceleration, and an example of AI-assisted AWS infrastructure creation. Sponsored by Versus Incident and maintained by the DevOps VN community, the repository is licensed under MIT. All resources are available as markdown documents, requiring no installation.
The repository serves as a knowledge base with a collection of markdown documents and guides for implementing AI in DevOps contexts. It covers topics such as an AI roadmap (01-ai-roadmap-for-devops), MCP server building (02-mcp-for-devops), AI Agent development (03-ai-agent-for-devops), SRE Agent monitoring (04-ai-agent-for-monitoring), AI Project Management (05-ai-project-management), and SRE Agent Runbook Brain (06-sre-agent-brain). Additional resources include team AI guidelines, mock interview preparation, a list of 10 AI prompts, and a guide to accelerate AWS certification with AI. The documents aim to help users understand and apply AI tools to optimize cloud infrastructure, automate monitoring, and improve incident response workflows.
- DevOps engineers seeking to learn AI skills and plan career growth.
- Team leads aiming to establish safe AI implementation guidelines and training materials for their teams.
- Individuals wanting to boost daily productivity using AI prompts for infrastructure code generation.
- Job seekers preparing for AI-related DevOps interviews using the provided mock interview guide.
- Cloud engineers looking to accelerate AWS certification using AI tools.
How do you install or deploy this agent?
As a documentation repository, there is no installation required. Users can access the content directly on GitHub or clone it locally using the command: git clone https://github.com/VersusControl/devops-ai-guidelines.gitHow do you use this agent?
Users can follow the Quick Start Paths based on their role: Individual DevOps Engineer starts with [10 AI Prompts](./resources/10-ai-prompts-devops.md), Team Lead/Manager reviews [Team Guidelines](./resources/ai-guidelines-devops-team.md), and Organization/CTO implements [AI Guidelines](./resources/ai-guidelines-devops-team.md) across teams. All documents are accessible via links in the repository's README.
What are this agent's strengths and limitations?
- Offers a complete learning roadmap from beginner to advanced AI Infrastructure Architect over 18 months.
- Covers diverse topics including MCP, AI Agents, SRE Agents, and project management.
- Provides practical resources for teams and individuals, such as interview prep, prompts, and team guidelines.
- All resources are theoretical guides without runnable code examples.
- Dependent on AWS and Golang, which may not suit all DevOps environments.
- No online platform or interactive tutorials; users must read markdown documents.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| DevOps AI Guidelines & Learning Path This agent | 25 · Major gaps | Library / SDKFree | ★ 1.6k | 25d ago | Python | — |
| AI DevOps Framework (aidevops) | 65 · Some gaps | CLIFree + model costs | ★ 411 | today | Shell | OpenAI API · Claude API |
| Tons of Skills — Claude Code Plugins Marketplace | 28 · Major gaps | Agent plugin / skillFree | ★ 2.9k | today | Python | Claude Code |
| OpenOcta (Octopus) | 27 · Major gaps | Desktop appFree + model costs | ★ 3.2k | 1mo ago | TypeScript | — |
How does FollowAgents rate this agent?
Why each dimension lost points
The evidence shows a repository of documentation and code examples, with no actual execution permissions or data flows. No mention of least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. Hence all trust criteria score 0.
Self-consistency: README and code examples are structurally consistent, but full documentation is missing, and some links may point to non-existent files. Dependency availability: Code examples depend on external libraries (e.g., langchain) but no dependency manifest or installation instructions are provided. Failure messages: Basic error handling exists in test code, but not in production code.
Audience and scenarios: README clearly lists quick-start paths for different roles (individual, team, organization), but detailed scenarios are not provided. Capability boundaries: Documentation describes learning paths and guidelines but does not clearly define the Agent's capability boundaries. Trigger precision: No trigger mechanisms are mentioned. Environment fit: Code examples target specific environments (e.g., Golang, Kubernetes) but no environment configuration instructions are provided.
Information architecture: README provides a clear directory structure, but some documents may be missing. Install notes: No installation guide is provided. Naming stability: Document titles and links are consistent in README, but no version control is mentioned. Examples and FAQ: Multiple example documents are provided, but no FAQ. Known limitations: Not mentioned. License: MIT license is clear. Versioning/changelog: Not provided. Maintenance responsibility: README mentions community maintenance but does not specify responsible parties.
Output usability: Documentation provides learning paths and guidelines, but no directly usable outputs. Marginal value: Provides guidance on AI in DevOps, which has some value. Cost-benefit: No cost or benefit analysis is mentioned.
Claim traceability: Claims in README lack sources or evidence. Cross-source corroboration: No verification from other sources. Fact-inference separation: Facts and inferences are not distinguished.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- The repository is primarily documentation and example code, not a complete Agent product; note its scope during assessment.
- Code examples depend on external APIs and libraries, but no dependency management or security audit is provided.
- Links in README may point to non-existent files; verify them.