Training course · OpenAI Codex · Legacy Modernisation

OpenAI Codex for Legacy Modernisation

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 Legacy Modernisation” is designed for roles such as Developers maintaining COBOL, PL/I or RPG systems, Java and .NET teams with ageing monoliths and outdated frameworks and Software and enterprise architects in modernisation programmes and shows step by step how to use OpenAI Codex productively, safely and transparently in Legacy Modernisation – from the fundamentals to advanced workflows.

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.

Many business-critical systems still run on mainframe COBOL, ageing Java EE applications or .NET Framework solutions whose original developers moved on long ago. AI coding assistants and agents make these codebases far more approachable: they explain programme logic, reconstruct business rules, generate characterisation tests and support stepwise migration to modern languages, frameworks and cloud platforms. Successful modernisation is still engineering work, though, because automated translation without understanding simply produces new legacy code. In this course you learn to apply AI along proven patterns such as strangler fig, branch by abstraction and anti-corruption layers, lock down existing behaviour with tests before every change and base modernisation decisions on a solid analysis of what you already have.

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?

  • Developers maintaining COBOL, PL/I or RPG systems
  • Java and .NET teams with ageing monoliths and outdated frameworks
  • Software and enterprise architects in modernisation programmes
  • IT managers and project leads responsible for migrations
  • Application modernisation and cloud migration consultants

Typical challenges we address

  • Business rules are buried undocumented in code and the original developers’ knowledge is gone
  • Missing automated tests make every change risky
  • Outdated frameworks, unpatched libraries and proprietary runtimes
  • Big-bang migrations fail on scope, data migration and parallel operation

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 to understand legacy systems, document business rules and make dependencies visible.
  • Protect existing behaviour with generated characterisation tests before migrating or refactoring code.
  • Plan and steer incremental modernisation using proven patterns instead of risky big-bang cutovers.

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: Legacy Modernisation
  • Extracting business rules
  • Mapping dependencies
  • Generating characterisation tests
  • Framework upgrades
  • Translating and refactoring code
  • Designing a strangler fig architecture
Module 7 Data protection, quality & transfer to daily work
  • Legacy systems often process customer, financial or insurance data: you learn to sanitise code and test data before using AI, clarify licensing and copyright questions around translated code and address the requirements of the GDPR, DORA and sector regulators for changes to critical systems.
  • 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

Extracting business rules

Analyse COBOL paragraphs, copybooks or Java service classes, document the embedded calculation and decision logic as readable business rules and validate them together with domain experts.

Mapping dependencies

Reconstruct call graphs, JCL job chains, database access and interfaces from source code and visualise them as diagrams to find sensible seams for stepwise replacement.

Generating characterisation tests

Capture current behaviour with golden master and approval tests, derive anonymised test data from production patterns and build a safety net before any migration begins.

Framework upgrades

Plan migrations from Java EE to Jakarta EE or Spring Boot and from .NET Framework to modern .NET, identify API breaks and apply consistent changes across many files.

Translating and refactoring code

Turn COBOL modules into idiomatic Java or C# rather than translating line by line, map data types such as packed decimals correctly and verify the result against the original with comparison tests.

Designing a strangler fig architecture

Design facades, routing layers and anti-corruption layers through which new services take over legacy functions step by step, including data synchronisation and fallback options. Each stage remains independently deployable.

Possible hands-on projects

Copybook to schema converter

A tool that reads COBOL copybooks and generates JSON schemas, Java records or SQL tables, correctly mapping decimal and REDEFINES fields.

Legacy knowledge portal

A small web app that makes analysed programmes, business rules and dependencies searchable and answers questions about the legacy system with source references.

Comparison test harness

A test harness that sends identical inputs to the old and new system, compares outputs field by field and logs discrepancies clearly.

Prerequisites

You have hands-on programming experience, are comfortable with Git and the command line, and know pull-request-based workflows. You have programming experience in at least one language of the legacy or target platform, such as COBOL, Java or C#, plus a basic grasp of software architecture.

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

Legacy systems often process customer, financial or insurance data: you learn to sanitise code and test data before using AI, clarify licensing and copyright questions around translated code and address the requirements of the GDPR, DORA and sector regulators for changes to critical systems.

Suggested dates

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

  • Mon, 23 November 2026 – Tue, 24 November 2026 Online Request
  • Mon, 30 November 2026 – Tue, 1 December 2026 Augsburg Request
  • Tue, 15 December 2026 – Wed, 16 December 2026 Online Request
  • Wed, 13 January 2027 – Thu, 14 January 2027 Online Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Kaiserslautern Request
  • Mon, 8 February 2027 – Tue, 9 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.

Can AI automatically translate a COBOL system into Java?

AI translates individual modules far faster than manual work, but a pure one-to-one translation often yields code that is hard to maintain. A better approach is to understand and test the business rules first and design the target architecture deliberately. In the course you combine AI-assisted translation with comparison tests and refactoring.

How do we handle very large codebases?

Large legacy systems do not fit entirely into a model’s context. What works is systematic inventory, per-module summaries, dependency graphs and targeted retrieval of relevant code. Agents then work module by module with a clearly defined brief. You learn to build this structure and turn the results into input for architecture decisions.

How much does “OpenAI Codex for Legacy Modernisation” 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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