Training course · Agentic Coding · Legacy Modernisation

Agentic Coding for Legacy Modernisation

Use autonomous coding agents effectively: specs instead of prompts, task decomposition, test-driven loops, multi-agent setups and governance.

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

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for roles such as Developers maintaining COBOL, PL/I or RPG systems, Java and .NET teams with ageing monoliths and outdated frameworks and Software and enterprise architects in modernisation programmes and shows step by step how to use Agentic Coding productively, safely and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

Agentic coding means working with AI agents that carry out development tasks largely on their own: they read code, plan changes, run commands and tests, and correct themselves based on the results. Tools such as Claude Code, OpenAI Codex, the GitHub Copilot coding agent and Cursor’s agent mode have made this way of working practical for everyday use. This vendor-neutral course teaches the underlying methodology so that you achieve reliable results whatever the tool, and redefine the developer’s role within your engineering team.

The core shift is from ad hoc prompts to robust specifications. You will learn to decompose and describe tasks so that agents solve them reliably, to set up test-driven loops in which tests act as objective acceptance criteria, and to run several agents in parallel or with a division of labour. We also cover review strategies for agent-generated code and team governance: permissions, security boundaries, accountability, cost control and the gradual introduction of agentic workflows into your existing development processes.

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 understand how coding agents work and choose the right tool and level of autonomy.
  • You write specifications and project context that agents can work with reliably.
  • You break initiatives into verifiable sub-tasks and steer agents through test-driven loops.
  • You use multi-agent setups and parallel work in a controlled way.
  • 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 Foundations of agentic development
  • How coding agents work: tool calls, context windows and feedback loops
  • Comparing tools: terminal agents, IDE agents and cloud agents
  • Recognising suitable tasks and choosing levels of autonomy deliberately
  • The new role in the team: from implementation to steering and acceptance
Module 2 Specifications instead of prompts
  • Describing tasks with goal, context, constraints and acceptance criteria
  • Keeping project context in files such as CLAUDE.md, AGENTS.md or rules
  • Plan-first approach: let the agent draft a solution and review it before implementation
  • Versioning specifications and maintaining them as part of the repository
Module 3 Task decomposition and test-driven agent loops
  • Breaking large initiatives into independent, verifiable sub-tasks
  • Having tests written first and using them as an objective stopping criterion
  • Using linters, type checks and builds as automatic feedback
  • Recognising stuck loops and restarting with a new strategy
Module 4 Multi-agent setups and parallel work
  • Defining specialised subagents for research, implementation, testing and review
  • Organising parallel work with branches, worktrees and isolated environments
  • Orchestration: merging results and resolving conflicts
  • Sharing tools and data sources through MCP

Day 2

Module 5 Review, security and team governance
  • Review strategies for large, agent-generated changes
  • Defining permissions, sandboxing and protection against prompt injection
  • Embedding agents in CI pipelines and pull request processes
  • Planning team guidelines, accountability, costs and a phased rollout
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 several years of development experience, are confident with Git, testing and CI, and ideally have already used AI coding tools. 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 Git, a terminal and your development environment, plus access to at least one coding agent such as Claude Code, Codex, Copilot or Cursor.

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
  • Wed, 2 December 2026 – Thu, 3 December 2026 Augsburg Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Online Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Kaiserslautern Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Online Request
  • Thu, 4 February 2027 – Fri, 5 February 2027 Bremen Request

Frequently asked questions

How does agentic coding differ from vibe coding?

Vibe coding relies on fast, intuitive iteration in natural language and is particularly suited to prototypes. Agentic coding is an engineering discipline for professional codebases: precise specifications, tests as acceptance criteria, clear permissions and structured reviews ensure that agents deliver reliable, maintainable results even in complex, long-lived projects.

Is the course tied to a specific tool?

No. The course teaches methods that work with Claude Code, OpenAI Codex, the GitHub Copilot coding agent, Cursor and comparable tools. The exercises highlight differences and common ground, for example in context files, permissions and subagents. You can work with whichever tool your organisation already uses or plans to adopt.

How do we stay in control of quality and security with autonomous agents?

Control comes from several layers: tightly scoped permissions and sandboxing, tests and static analysis as automatic checks, mandatory human review before merging, and clear team rules on accountability. During the course you develop a governance concept that fits your processes, security requirements and regulatory obligations.

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 “Agentic Coding 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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