Training course · Agentic Coding · Java & Spring

Agentic Coding for Java and Spring Developers

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 Java and Spring Developers” is a hands-on course for roles such as Java back-end developers using Spring Boot or Jakarta EE, Teams modernising legacy Java applications and Software architects for enterprise and microservice landscapes. Rather than learning Agentic Coding in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Java & Spring.

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.

Java and Spring carry business-critical systems in many organisations: banking back ends, ERP integrations, public-sector applications and large microservice estates. These systems have often grown over many years, and modernisation never stops. AI coding assistants and agents change the heaviest routine work in particular: major Spring Boot upgrades including the javax-to-jakarta switch, JUnit and Mockito tests for poorly covered code, refactorings towards records and modern language features, and analysis of stack traces and Hibernate issues. Clear guardrails are essential: build tool, module structure, architecture rules and test strategy all need to be in context. This course shows you how to use agents productively and in a controlled way in Maven or Gradle projects.

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?

  • Java back-end developers using Spring Boot or Jakarta EE
  • Teams modernising legacy Java applications
  • Software architects for enterprise and microservice landscapes
  • Developers in banking, insurance and the public sector
  • Tech leads responsible for upgrades and technical debt

Typical challenges we address

  • Laborious framework and JDK upgrades with numerous breaking changes
  • Large legacy codebases with low test coverage and tight coupling
  • Hard-to-analyse performance issues in JPA, Hibernate and transactions
  • Heavy boilerplate in configuration, mapping and interface code

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 for Spring Boot upgrades, refactorings and test generation in Maven and Gradle projects.
  • Diagnose Hibernate, performance and production issues faster with AI support.
  • Embed architecture rules and quality standards so that generated Java code adheres to them reliably.

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: Java & Spring
  • Driving a Spring Boot upgrade
  • Generating JUnit and Mockito tests
  • Refactoring legacy code
  • Analysing JPA and Hibernate issues
  • REST APIs and interfaces
  • Making sense of stack traces and production errors
Module 7 Data protection, quality & transfer to daily work
  • Java systems often run in regulated sectors: agree with information security and data protection which code may be sent to external models, and take requirements such as DORA, NIS2 or national banking IT rules into account. Generated code and new Maven dependencies should be checked for licences and known vulnerabilities and recorded in an SBOM.
  • 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

Driving a Spring Boot upgrade

The agent analyses dependencies, applies OpenRewrite recipes, switches javax packages to jakarta, adapts deprecated configuration and security setups and iterates over build and tests until the application is green again.

Generating JUnit and Mockito tests

Services and controllers get unit tests with JUnit and Mockito plus slice tests using WebMvcTest or DataJpaTest. The AI deliberately raises coverage on critical paths and documents test assumptions.

Refactoring legacy code

Large classes are split up, DTOs turned into records, null checks replaced with Optional and streams used where they help. Existing tests safeguard behaviour at every step.

Analysing JPA and Hibernate issues

The AI reads SQL logs and stack traces, spots N+1 queries, LazyInitializationExceptions and poor fetch strategies, and proposes entity graphs, projections or index changes. Each optimisation is backed by SQL logs.

REST APIs and interfaces

OpenAPI specifications are turned into controllers, DTOs, MapStruct mappers and Bean Validation, complete with consistent error handling via ControllerAdvice and integration tests. Existing package and naming conventions are respected.

Making sense of stack traces and production errors

Long stack traces, thread dumps and log excerpts are summarised, the likely root cause is narrowed down and a reproducing test case plus fix is proposed.

Possible hands-on projects

Spring Boot microservice

A REST service with Spring Data JPA, Flyway migrations, Bean Validation, Actuator endpoints and Testcontainers tests, developed entirely by an AI agent from a specification.

Dependency report for Maven and Gradle

A command-line tool that analyses build files, lists outdated and vulnerable libraries and describes upgrade paths along with expected breaking changes.

Architecture rule checks

An ArchUnit test suite that automatically enforces your project’s layering rules, package dependencies and naming conventions and runs in the CI pipeline.

Prerequisites

You have several years of development experience, are confident with Git, testing and CI, and ideally have already used AI coding tools. In-depth Java knowledge and practical experience with Spring Boot or Jakarta EE, Maven or Gradle and Git.

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

Java systems often run in regulated sectors: agree with information security and data protection which code may be sent to external models, and take requirements such as DORA, NIS2 or national banking IT rules into account. Generated code and new Maven dependencies should be checked for licences and known vulnerabilities and recorded in an SBOM.

Suggested dates

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

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Bremen Request
  • Tue, 12 January 2027 – Wed, 13 January 2027 Online Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Salzburg Request
  • Mon, 1 February 2027 – Tue, 2 February 2027 Online Request
  • Wed, 17 February 2027 – Thu, 18 February 2027 Kaiserslautern 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 an AI agent carry out a major Spring Boot upgrade on its own?

An agent can take on much of the mechanical work – package switches, configuration changes and iteratively fixing compile and test failures – especially when combined with OpenRewrite. Architecture decisions, functional sign-off and testing in realistic environments remain the team’s job. On the course you practise this workflow on a sample project.

Is AI-assisted coding allowed in regulated environments?

In principle yes, but with clear rules. What matters is the choice of provider and operating model, contractual commitments on data use, approval processes and traceable reviews. We discuss how banks, insurers and public authorities typically approach this and which questions you should settle with your information security team.

How much does “Agentic Coding for Java and Spring Developers” 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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