Course · Training · Workshop
Agentic (AI-Assisted) Software Engineering
Software development with an agentic workflow: peer-programming with generative AI that directly modifies, tests, builds and runs code and uses tools such as the shell.
This innovative course introduces agent-based software development where AI functions as an active peer programmer. Participants learn how to establish continuous collaboration with an established IDE Extension, where the AI directly modifies, generates, tests, builds, and executes code. The course covers advanced strategies such as planning & acting phases, rules formulation, prompt techniques, context management, and extending agent capabilities through MCP servers. Participants will work in existing software projects with single page application frontends, REST backends, databases and API integrations, and look at fixing issues, debugging, implementing new features, understanding code and building a project specific agentic workflow context framework. Participants will also build their own little MCP server, to get a strong understanding of how models can be given access to tool usage. There will be an overview on how to select models, choose providers or approach internal hosting. The course focuses on a well established, open source, vendor and model provider independent AI integration in Visual Studio Code. Alternative AI focused IDE's, Plugins or Integrations will be discussed. To make the maximum use of time, we'll focus on one tool with different models, but the concepts, workflows and approaches are meant to be transferable to any tool with the same or even stronger capabilities.
What participants say
The “AI Assisted Engineering On-Boarding” course was an outstanding experience. It provided a clear, practical, and inspiring introduction to how AI can support modern engineering work. The content was well structured, easy to follow, and highly relevant to real day-to-day challenges. What stood out most was the strong balance between theory and hands-on application. The course not only explained the concepts clearly, but also showed how to use AI tools effectively and responsibly in actual engineering scenarios. It made complex topics feel accessible and immediately useful. I especially appreciated the engaging format, the high quality of the materials, and the clear expertise of the instructors. The course created a motivating learning environment and gave me valuable confidence in applying AI-assisted approaches in my work. Overall, I can highly recommend “AI Assisted Engineering On-Boarding” to anyone who wants a practical, forward-looking, and well-designed introduction to AI in engineering. It is an excellent course that delivers real value from start to finish.
Leiter IT Entwicklung Platform & Services
The AI Assisted Engineering On-Boarding course was very practical and easy to follow and awesome! I especially appreciated the hands-on approach and the focus on real use cases. It gave me a clear understanding of how to work effectively with Claude Code in a development context. After the course, I felt much more confident getting started and applying it in daily work.
Frontend Web Developer
The course fully met all my expectations and provided a smooth introduction to the subject. With the help of the instructor and the course materials, I was able to easily overcome any initial hurdles, allowing me to get started using the agents as quickly as possible.
Senior Software Engineer
I really enjoyed the lessons and learned a lot of new stuff on this course. It teaches not only fundamentals but also how to be prepared for your next challenges. The tooling that is used is very sturdy, works very well in the lab setup and is very diverse, you can pick what you like in your projects. And most importantly: I did not write any piece of code myself, the agents did everything!
Software Engineer I
Great training on agentic software development. The trainer obviously had lots of hands-on experience with the topic, there was no question that he couldn't answer. Lab environment was very well thought through and helped a lot in practicing what we learned.
Senior Software Design Engineer
Very concrete and practical training on "Agentic Software Engineering", with a good amount of theoretical background to understand what happens behind the scenes. The instructor was very knowledgeable and willing to answer the multitude of questions.”
Software Engineer I Karakun
These customers booked courses in the same topic cluster.More customers →
Content
The course will consist of the following topics and may be extended or adapted based on the audience. The examples in the course will focus on widely used programming languages, software architectures and frameworks. For in-house courses there is a selection of programming languages and frameworks which can be chosen to better fit the audience.
- Introduction to Agentic Software Development
- Evolution from “one-shot prompting” to agentic workflow peer programmers
- Understanding the agentic workflow paradigm
- Overview of course environment and tools
- Fundamentals of IDE-Integrated AI Agents
- Setting up and configuring the IDE extension
- Understanding planning vs. acting modes
- Effective communication patterns with AI agents
- Context Management and Rule Formulation
- Building effective project context
- Defining clear rules and guidelines
- Advanced prompt engineering techniques
- Working with Existing Codebases
- Strategies for introducing AI agents to complex projects
- Code comprehension and documentation generation
- Refactoring and optimization with agent assistance
- Practical Development Tasks
- Debugging and fixing issues with AI agents
- Implementing new features in existing applications
- Test creation and validation
- Frontend Development with AI Agents
- Single page application component development
- UI/UX visual validation and improvement
- Browser use and end to end testing
- Backend Development with AI Agents
- REST API design and implementation
- Agentic API testing approaches
- Database integration and interaction
- Use and build MCP Servers
- Understanding the Model Context Protocol
- Implementing tool-using capabilities
- Integrating external services and APIs
- Model Selection and Deployment
- Comparing different AI models and capabilities
- Cloud providers vs. self-hosting considerations
- Performance and cost optimization
- Advanced Agent Interaction Techniques
- Multi-step reasoning and planning
- Handling complex requirements
- Error recovery and iterative improvement
- Building Project-Specific “Agentic Tooling Framework”
- Sharable rules, context and tooling
- Agentic empowering testing setup
- AI powered dev-containers
- Future Trends and Best Practices
- Emerging technologies in AI-assisted development
- Security considerations
- Team collaboration with AI agents
The course focuses on a well established, open source, vendor and model provider independent AI integration in Visual Studio Code. Alternative AI focused IDE’s, Plugins or Integrations will be discussed. To make the maximum use of time, we’ll focus on one tool with different models, but the concepts, workflows and approaches are meant to be transferable to any tool with the same or even stronger capabilities.
The actual course content may differ from the above depending on the trainer, delivery, duration and the composition of participants.
Request this course in-house
Request a public date
No suitable public date? Register without obligation — once there is enough interest we schedule a new public date and let you know first.
More about Agentic Software Engineering
The Agentic Workflow in software development represents a paradigm shift in how we create software. With modern AI tools, we establish a working method where AI agents not only make suggestions but actively participate in the development process – directly modifying, testing, building, and executing code. This peer programming method combines the creativity and expertise of human developers with the efficiency and analytical strength of AI systems, leading to faster development, higher code quality, and more innovative solutions.Further resources:
History
AI-assisted coding began with simple autocomplete systems in the 2010s. With the introduction of GitHub Copilot in 2021, large-scale AI assistance entered development environments. These early tools operated on a "one-shot" principle: the developer writes a prompt, the AI delivers a suggestion, and the developer decides. Multi-step tasks, error correction, and tool use were beyond their capabilities.
With the introduction of the Model Context Protocol (MCP) by Anthropic in 2024 and the emergence of powerful agent frameworks, the picture changed fundamentally. AI agents can now autonomously operate terminals, run tests, analyze error messages, and iteratively develop solutions. GitHub Copilot gained an "Agent Mode" in 2025 that independently iterates on code, recognizes errors, and corrects them – a sign that agentic coding has become the new standard.