Training course · OpenAI Codex · DevOps & Cloud

OpenAI Codex for DevOps and Cloud Teams

Delegate development work with OpenAI Codex: CLI and cloud agent, AGENTS.md, sandboxing, approvals and team PR workflows.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects OpenAI Codex with the everyday processes of DevOps & Cloud. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

OpenAI Codex is a coding agent that runs both locally, as a command-line tool and IDE extension, and as a cloud agent. Locally, Codex reads your repository, proposes changes and runs commands inside a sandboxed environment. In the cloud, it works on delegated tasks in parallel inside isolated containers and returns results as traceable diffs or pull requests. Over two days you will learn how to combine both modes effectively and how to scope tasks so that the agent can complete them reliably and with minimal back-and-forth.

Control and security sit at the heart of the course. With AGENTS.md you hand Codex project knowledge, build and test commands, and conventions. You configure approval modes and sandboxing so that the agent only does what you permit, and you set up cloud environments with suitable setup scripts. You will also integrate Codex into your pull request workflows, use automated code review, and define team rules on which tasks are delegated and how results are checked before they are merged into the main branch.

DevOps and cloud teams own infrastructure as code, CI/CD pipelines, monitoring, cost control and security – often across several cloud providers and on-premises environments. Much of the work is configuration in HCL, YAML and shell, troubleshooting through logs and cross-checking documentation. AI coding assistants and agents speed up exactly that: they write reusable Terraform modules, fix failing pipelines, create Ansible roles, analyse incident logs and propose cost optimisations. At the same time the stakes are high, because a single wrong command can hit production. This course teaches you to use agents productively with clear permission boundaries, plan-and-review workflows and safeguarded environments.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • DevOps engineers and platform teams
  • Cloud architects working with AWS, Azure or Google Cloud
  • Site reliability engineers responsible for operations and incidents
  • System administrators moving towards infrastructure as code
  • Developers who maintain their own CI/CD pipelines

Typical challenges we address

  • Growing volumes of Terraform, YAML and pipeline configuration with drift and duplication
  • Time-consuming troubleshooting of failing pipelines and distributed logs
  • Rising cloud costs without clear visibility
  • High demands on security, compliance and traceability of changes

What you will be able to do

  • You use Codex locally and in the cloud for the development tasks each mode suits best.
  • You write an AGENTS.md that guides the agent reliably through your project.
  • You configure approval modes and sandboxing to match your security requirements.
  • You delegate tasks to the cloud agent and review the results systematically.
  • Use AI agents safely for infrastructure as code, pipelines and configuration management.
  • Analyse incidents, pipeline failures and cloud costs faster with AI support.
  • Define permission boundaries and approval processes so agents cannot make uncontrolled changes to production.

Course outline

Day 1

Module 1 Codex at a glance: CLI, IDE and cloud
  • Comparing how Codex works locally and in the cloud
  • Installing the Codex CLI, signing in and starting it in a repository
  • Interactive sessions, non-interactive execution and model choice
  • Distinguishing tasks that suit a coding agent from those that do not
Module 2 Project knowledge with AGENTS.md
  • Building an AGENTS.md with build, test and lint commands
  • Stating conventions, architectural notes and prohibitions precisely
  • Using nested AGENTS.md files for monorepos and sub-projects
  • Checking effectiveness and refining instructions iteratively
Module 3 Sandboxing, approvals and security
  • Choosing approval modes deliberately, from read-only to largely autonomous
  • Understanding sandbox boundaries for file system and network access
  • Handling secrets, dependencies from the internet and prompt injection
  • Configuration files for team standards and safe defaults
Module 4 Delegating tasks to the cloud agent
  • Configuring cloud environments with setup scripts and dependencies
  • Writing tasks with a clear goal, context and acceptance criteria
  • Launching several tasks in parallel and comparing the results
  • Using logs and test output to understand the proposed solutions

Day 2

Module 5 PR workflows and code review in the team
  • Turning results into pull requests and requesting follow-up changes
  • Using automated Codex code review on GitHub
  • Embedding Codex in scripts and CI pipelines
  • Setting team rules for delegation, review duties and accountability
Module 6 Practice lab: DevOps & Cloud
  • Developing Terraform modules
  • Fixing CI/CD pipelines
  • Ansible and configuration management
  • Incident analysis
  • Optimising cloud costs
  • Policy as code and hardening
Module 7 Data protection, quality & transfer to daily work
  • Agents with access to cloud accounts and pipelines are a security matter: work with least privilege, separate environments and human approval, and keep cloud credentials, state files and secrets out of prompts. Operators of essential and important services must also meet NIS2 requirements on risk management, supply-chain security and traceability of changes.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Developing Terraform modules

The agent creates reusable Terraform or OpenTofu modules with variables, validations and outputs, runs fmt, validate and plan, and explains every planned change before a human approves the apply.

Fixing CI/CD pipelines

Failed GitHub Actions, GitLab CI or Azure DevOps runs are analysed from their logs, root causes such as caching issues or missing permissions identified and corrected pipeline definitions proposed.

Ansible and configuration management

The AI writes idempotent Ansible roles, templates and inventories, replaces fragile shell scripts and adds Molecule tests so configuration stays reproducible. Existing roles are gradually standardised along the way.

Incident analysis

Logs, metrics and alerts from monitoring are brought together, a timeline reconstructed, likely causes narrowed down and a draft postmortem with actions produced. The team keeps the final judgement.

Optimising cloud costs

The assistant reviews cost reports and resource definitions, finds oversized instances, orphaned volumes or missing lifecycle rules and drafts the matching IaC changes. Each recommendation includes estimated savings.

Policy as code and hardening

IaC code is scanned with tools such as Checkov or tfsec, findings are fixed and rules for encryption, tagging or network access are expressed as policy as code.

Possible hands-on projects

Drift detector

A script that regularly runs terraform plan across several environments, detects drift and posts a summary to a chat channel.

Pipeline template library

Reusable CI/CD templates for build, test, security scanning and deployment, documented and accompanied by sample projects. Teams adopt them with just a few lines of configuration.

Cost report tool

A CLI that pulls cost data from a cloud account, breaks it down by tags and teams and clearly highlights optimisation candidates.

Prerequisites

You have hands-on programming experience, are comfortable with Git and the command line, and know pull-request-based workflows. Practical experience with Linux, Git and at least one cloud platform, plus a working knowledge of Terraform, containers and CI/CD.

Technical requirements

Your own laptop with a terminal, Git and a Node.js runtime; a ChatGPT plan with Codex access or an OpenAI API key, plus a GitHub account for the cloud exercises.

Data protection & compliance

Agents with access to cloud accounts and pipelines are a security matter: work with least privilege, separate environments and human approval, and keep cloud credentials, state files and secrets out of prompts. Operators of essential and important services must also meet NIS2 requirements on risk management, supply-chain security and traceability of changes.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Bremen Request
  • Mon, 18 January 2027 – Tue, 19 January 2027 Online Request
  • Wed, 3 February 2027 – Thu, 4 February 2027 Salzburg Request
  • Wed, 17 February 2027 – Thu, 18 February 2027 Online Request
  • Wed, 3 March 2027 – Thu, 4 March 2027 Kaiserslautern Request

Frequently asked questions

When should I use Codex locally and when in the cloud?

Locally you work interactively and close to the code, for example when debugging or making exploratory changes where you want to follow every step. The cloud agent suits well-defined tasks that can run independently, such as adding tests or fixing smaller bugs. You can start several of these in parallel and review each one as a pull request.

What belongs in a good AGENTS.md?

A good AGENTS.md explains how the project is built, tested and checked, which conventions apply and which areas must not be touched. It is concise, concrete and kept up to date. Notes on architecture and common pitfalls also help. During the course you write an AGENTS.md for a project of your own and test how it changes the agent’s behaviour.

How safe is it to let Codex run commands?

By default Codex works inside a sandbox with restricted file and network access and asks for approval before risky steps. You decide how much autonomy to grant through approval modes and configuration. In the course you learn to choose these settings deliberately and to limit risks such as prompt injection or running unfamiliar dependencies.

Should an AI agent be allowed to apply infrastructure changes itself?

That is something to limit deliberately. A proven workflow has the agent write code and run plan, a human review the result and the apply run through the regular pipeline. On the course you learn to design permissions, sandbox environments and approvals so that production systems stay protected.

Which cloud platform does the course use?

The methods are vendor-neutral and transfer to AWS, Azure and Google Cloud. Exercises use examples you can reproduce with local tooling or test accounts. You are welcome to bring scenarios from your own platform, provided no production credentials are used.

How much does “OpenAI Codex for DevOps and Cloud Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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