Training course · OpenAI Codex · Tech Leads & Engineering Managers

OpenAI Codex for Tech Leads and Engineering Managers

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 Tech Leads & Engineering Managers. 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.

AI coding assistants and autonomous agents are changing how engineering teams work: tasks are sliced differently, effort shifts from writing code to reviewing it, and deciding which work can be delegated to agents becomes a leadership responsibility. Tech leads and engineering managers have to select tools, set policies, enable their teams and measure impact without losing sight of code quality, security and the growth of junior engineers. In this course you learn to plan the rollout of AI coding tools in a structured way, establish shared working practices and rule files for your team, choose meaningful metrics beyond raw speed and apply AI to leadership tasks such as planning, onboarding and technical communication. Deep programming skills are not required.

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?

  • Tech leads and lead developers
  • Engineering managers and software team leads
  • Heads of engineering and CTOs in mid-sized companies
  • Delivery and project leads in IT organisations
  • Developer experience and enablement leads

Typical challenges we address

  • Unclear policies lead to a patchwork of tools and shadow AI within the team
  • More generated code increases review load and the risk of technical debt
  • Difficulty measuring the real benefit of AI tools in a credible way
  • Concerns about junior engineers’ skill development and team buy-in

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.
  • Plan the rollout of AI coding tools in a structured way with clear policies, roles and guardrails.
  • Measure the impact on delivery, quality and satisfaction with credible metrics.
  • Use AI agents yourself for leadership tasks such as planning, onboarding and technical communication.

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: Tech Leads & Engineering Managers
  • Writing a team AI policy
  • Rule files and team conventions
  • Measuring impact
  • Slicing work for agents
  • Faster onboarding
  • Technical communication
Module 7 Data protection, quality & transfer to daily work
  • As a leader you own the legal framework: you learn to address licensing and copyright questions around generated code, protection of source code and secrets, works council co-determination rights when measuring performance and obligations under the GDPR and the EU AI Act in your team policies.
  • 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

Writing a team AI policy

Draft a practical guideline that clearly defines approved tools, permitted data classes, review obligations for generated code, handling of secrets and accountabilities, illustrated with concrete examples.

Rule files and team conventions

Build shared context files for coding agents covering architectural principles, testing strategy, style rules and banned patterns, keep them under version control and ensure consistent output across the team.

Measuring impact

Collect metrics such as DORA measures, pull request cycle time, change failure rate and developer satisfaction before and after rollout, and prepare the analysis with AI support.

Slicing work for agents

Write and break down backlog items so they can be delegated to agents with clear acceptance criteria, and decide which tasks deliberately stay with people.

Faster onboarding

Prepare codebases, architecture decisions and operational knowledge for new team members, create guided exploration tasks and have questions about the codebase answered with source references.

Technical communication

Draft incident post-mortems, roadmap updates, decision papers for senior management and release notes from tickets and commits, structured and tailored to each audience. This frees up time for actual leadership.

Possible hands-on projects

Team metrics dashboard

A small web app that pulls data from your Git platform and shows cycle times, review duration and deployment frequency over time.

PR summariser

A bot that collects all merged pull requests each week and turns them into a readable status report for stakeholders.

Onboarding guide generator

A script that analyses repository structure, README files and ADRs and produces a structured onboarding plan with learning tasks. It adapts to role and seniority.

Prerequisites

You have hands-on programming experience, are comfortable with Git and the command line, and know pull-request-based workflows. You lead a software team, either technically or as a line manager, and understand the development process; current hands-on coding is helpful but not required.

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

As a leader you own the legal framework: you learn to address licensing and copyright questions around generated code, protection of source code and secrets, works council co-determination rights when measuring performance and obligations under the GDPR and the EU AI Act in your team policies.

Suggested dates

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

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Bremen Request
  • Tue, 12 January 2027 – Wed, 13 January 2027 Salzburg Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Online Request
  • Mon, 1 February 2027 – Tue, 2 February 2027 Kaiserslautern Request
  • Wed, 17 February 2027 – Thu, 18 February 2027 Online 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.

How do we tell whether AI coding tools are worth it?

Don’t count generated lines of code; look at outcomes: lead time from idea to production, change failure rate, review effort, defect rates and team satisfaction. A baseline before rollout and accompanying qualitative surveys are essential. In the course you design a lean measurement approach that fits your organisation and involves employee representatives.

Do junior engineers still learn enough when AI writes the code?

The concern is valid and can be managed. Proven practices include having engineers explain every generated section in review, deliberate practice phases without an assistant and pairing with experienced colleagues where AI acts as a learning partner. You learn to adapt learning paths and review culture so that skill building and productivity go hand in hand.

How much does “OpenAI Codex for Tech Leads and Engineering Managers” 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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