Training course · Agentic Coding · Software Architecture

Agentic Coding for Software Architects

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 Software Architects” is designed for roles such as Software architects and solution architects, Enterprise architects with links to application development and Senior developers and tech leads with architectural responsibility and shows step by step how to use Agentic Coding productively, safely and transparently in Software Architecture – 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.

When AI agents write large parts of the code, architecture becomes more important, not less: clear module boundaries, documented decisions and verifiable quality goals are the frame that keeps generated code consistent. AI assistants also support architects directly, for example by analysing existing codebases, producing C4 diagrams, drafting architecture decision records and weighing alternatives against quality attributes. Fitness functions and architecture tests can be generated and anchored in your pipelines. In this course you learn to use AI as a sparring partner for architectural decisions, prepare architectural knowledge so that coding agents follow it reliably and spot erosion early in fast-growing codebases.

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?

  • Software architects and solution architects
  • Enterprise architects with links to application development
  • Senior developers and tech leads with architectural responsibility
  • Platform and cloud architects
  • Owners of architecture governance and review boards

Typical challenges we address

  • Architecture documentation goes stale quickly and drifts from the actual code
  • Decisions are made verbally and their context is lost
  • Gradual architectural erosion through cyclic dependencies and violated layer boundaries
  • Without clear guidance, AI-generated code ignores established patterns and conventions

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 to analyse existing systems, evaluate architectural options and record decisions as ADRs.
  • Anchor architecture rules as fitness functions in the pipeline and detect erosion early.
  • Shape context and rules so that AI coding agents reliably respect your architectural guidelines.

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: Software Architecture
  • Documenting architecture decisions
  • Reconstructing existing architecture
  • Evaluating alternatives
  • Generating fitness functions
  • Guardrails for coding agents
  • Carving out domains
Module 7 Data protection, quality & transfer to daily work
  • Architecture documents often describe security-relevant details such as network zones and data flows: you learn which information may be shared with AI services, how to assess licensing risks of components and how architectural decisions traceably reflect GDPR, NIS2 and Cyber Resilience Act requirements.
  • 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

Documenting architecture decisions

Turn discussions, meeting notes and pull requests into structured architecture decision records with context, options considered, decision and consequences, stored consistently in the repository. Related decisions are linked automatically.

Reconstructing existing architecture

Analyse existing codebases, extract modules, dependencies and interfaces and generate up-to-date C4 diagrams as diagrams-as-code with PlantUML, Mermaid or Structurizr. Diagrams can be refreshed automatically on every build.

Evaluating alternatives

Compare options such as a modular monolith, microservices or event-driven integration against quality scenarios, cost and team structure, and make trade-offs visible following ATAM principles.

Generating fitness functions

Express architecture rules such as layer access, forbidden dependencies or naming conventions as automated tests with ArchUnit, dependency-cruiser or similar tools and wire them into CI.

Guardrails for coding agents

Describe architectural principles, module boundaries and patterns in rule files and context documents so AI agents follow them reliably when generating code, and spot deviations in review.

Carving out domains

Derive bounded contexts, aggregates and context maps following domain-driven design from event storming results and business descriptions, and use them to shape teams and services.

Possible hands-on projects

ADR assistant

A command-line tool that turns bullet points into a complete ADR from a template, links related decisions and updates an index in the repository.

Dependency visualiser

A script that analyses import relationships in a codebase, flags cycles and layer violations and renders an interactive diagram in the browser.

Architecture Q&A over repos

A small application that indexes ADRs, diagrams and code and answers architecture questions with references to the underlying sources. New team members onboard faster.

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 several years of development experience and have made or supported architectural decisions in larger software systems.

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

Architecture documents often describe security-relevant details such as network zones and data flows: you learn which information may be shared with AI services, how to assess licensing risks of components and how architectural decisions traceably reflect GDPR, NIS2 and Cyber Resilience Act requirements.

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 Kaiserslautern Request
  • Wed, 9 December 2026 – Thu, 10 December 2026 Online Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Augsburg Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Online Request
  • Mon, 1 February 2027 – Tue, 2 February 2027 Salzburg 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 make architecture decisions?

AI is a strong sparring partner: it offers options, names trade-offs, finds gaps in reasoning and documents decisions in a structured way. Responsibility stays with people, because architecture depends on business context, team skills, budget and risk appetite. In the course you learn to use AI so that decisions are better reasoned and made faster.

How do we stop AI agents from eroding the architecture?

Through a combination of clear context and automated checks: rule files and ADRs describe module boundaries and patterns, fitness functions and architecture tests in CI block violations, and reviews focus on structural changes. Architecture thus turns from documentation into a verifiable guardrail for humans and agents alike.

How much does “Agentic Coding for Software Architects” 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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