Training course · OpenAI Codex · MVP Development for Startups

OpenAI Codex for Startups and MVP Development

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

“OpenAI Codex for Startups and MVP Development” is a hands-on course for roles such as Founders with little or no programming experience, Technical co-founders and first engineers in startups and Product managers in early-stage product teams. Rather than learning OpenAI Codex in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from MVP Development for Startups.

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.

For founders and early product teams, speed determines how fast they learn from the market. AI coding tools, vibe coding and app builders now make it possible to turn an idea into a working minimum viable product with sign-up, database, payments and a landing page within days, often without a large engineering team. The real challenge shifts as a result: which hypothesis are you testing, which features truly belong in the MVP, and how does a quick prototype become a codebase that withstands investor due diligence, the first paying customers and growth? In this course you learn to build MVPs purposefully with AI agents, manage technical debt deliberately, build in security and data protection from day one and prepare the handover to a professional engineering team.

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?

  • Founders with little or no programming experience
  • Technical co-founders and first engineers in startups
  • Product managers in early-stage product teams
  • Corporate innovation and venture building teams
  • Participants in incubator and accelerator programmes

Typical challenges we address

  • Limited time and budget to validate a product idea in the market
  • Lack of engineering capacity or dependence on external agencies
  • Quickly built prototypes with security holes, open databases and unclear architecture
  • Uncertainty about when to rewrite an MVP and when to evolve it

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.
  • Turn a business idea into a focused MVP scope and build it yourself with AI tools.
  • Integrate sign-up, database, payments and analytics and check your MVP for common security gaps before launch.
  • Manage technical debt deliberately and prepare the codebase for growth, due diligence and an engineering team.

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: MVP Development for Startups
  • From pitch to specification
  • Building the MVP by prompt
  • Payments and integrations
  • Landing pages and experiments
  • Pre-launch security check
  • Making the codebase hand-over ready
Module 7 Data protection, quality & transfer to daily work
  • Even an MVP processes real customer data: you learn to implement GDPR essentials such as privacy notices, data processing agreements and consent, manage API keys and secrets securely and clarify licensing and copyright questions around generated code that become relevant in due diligence.
  • 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

From pitch to specification

Translate the business idea, target group and core hypothesis into user stories, a data model and a prioritised scope that stays deliberately small and includes measurable learning goals for the market test.

Building the MVP by prompt

Generate a web app with sign-up, database, core feature and admin area step by step using AI agents or app builders, working in small, tested iterations with the code under version control.

Payments and integrations

Integrate subscriptions and one-off payments through a payment provider, transactional email, analytics and first integrations, with webhooks, error handling and securely stored API keys. Test and live modes stay separate.

Landing pages and experiments

Create landing pages, waitlists and pricing pages in several variants, track conversion events and run quick A/B tests to check demand and willingness to pay before investing in heavier development.

Pre-launch security check

Have the MVP code checked for open database rules, missing access controls, exposed keys and unprotected endpoints, and work through a short checklist for a safe go-live.

Making the codebase hand-over ready

Document the architecture, add tests, refactor critical areas and produce a technical overview that new engineers or a technical due diligence can follow quickly. This also helps investor conversations.

Possible hands-on projects

Waitlist landing page

A landing page with value proposition, email capture, double opt-in and a simple dashboard that analyses sign-ups and traffic sources.

Booking MVP

A small web app where customers book and pay for appointments, providers manage availability and both sides receive email confirmations.

Interview synthesis tool

A tool that collects notes from customer interviews, clusters statements by problems and wishes and shows evidence for or against the core hypothesis.

Prerequisites

You have hands-on programming experience, are comfortable with Git and the command line, and know pull-request-based workflows. You have a concrete product idea or work in an early-stage product team; no programming skills are required, though a basic technical understanding helps.

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

Even an MVP processes real customer data: you learn to implement GDPR essentials such as privacy notices, data processing agreements and consent, manage API keys and secrets securely and clarify licensing and copyright questions around generated code that become relevant in due diligence.

Suggested dates

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

  • Mon, 23 November 2026 – Tue, 24 November 2026 Online Request
  • Tue, 1 December 2026 – Wed, 2 December 2026 Kaiserslautern Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Online Request
  • Tue, 15 December 2026 – Wed, 16 December 2026 Bremen Request
  • Wed, 13 January 2027 – Thu, 14 January 2027 Online Request
  • Tue, 26 January 2027 – Wed, 27 January 2027 Salzburg 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.

Can a non-technical founder really build an MVP alone?

For many business models, yes: web apps with sign-up, data storage, a core feature and payments can be built with AI tools without programming skills. What matters are clear requirements, small steps and systematic testing. Complex logic, high security demands or rapid growth will eventually call for engineering expertise. In the course you learn to recognise that point.

Will an AI-built MVP have to be rewritten later?

Not necessarily. If you use version control from the start, keep the data model understandable, test core features and separate configuration from code, an MVP can often evolve step by step. We show which early decisions make later rework easier and how to tell when starting afresh is the cheaper option.

How much does “OpenAI Codex for Startups and MVP Development” 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.

More courses for Startups and MVP Development

More OpenAI Codex courses

All 32 OpenAI Codex courses

OpenAI Codex · MunichOpenAI Codex · SalzburgOpenAI Codex · BerlinOpenAI Codex · AugsburgOpenAI Codex · HanoverOpenAI Codex · BremenOpenAI Codex · StuttgartOpenAI Codex · KaiserslauternOpenAI Codex · Online