Generative AI is changing how we develop software - from individual code snippets to complete, agent-driven development processes. In this three-day training course, you will learn how to harness this transformation for your team.
You will learn how to use AI assistants and autonomous coding agents in a targeted manner: From intelligent requirements analysis and automated architecture design to autonomous development agents that implement complex features. You will always remain in control and understand where human expertise is indispensable.
After the training, you will be able to assess which AI tools and approaches are suitable for your specific challenges. You will know how to gradually move from simple code completion to advanced agent-based workflows.
Agenda
- Introduction & Context
- Why generative AI is shaping the future of software development
- AI concepts: tokens, autoregression, temperature, prompts, context, tools, model context protocol, memory
- Agents vs. assistants
- Introduction to agentic work
- Requirements
- AI-assisted creation, analysis, and refinement of requirements
- Automatically translate customer needs into structured requirements
- Generate epics and user stories from natural language and multimodal content
- Uniformly structured requirements with few-shot prompts
- Creating prototypes with agents without a technical background
- Architecture
- Designing system architectures with the help of AI agents
- AI-assisted decision-making and documentation
- Determining quality requirements from requirements and stakeholder interviews
- Detect contradictions in architecture with AI agents
- Identify and document technical debt with AI
- Identify deviations from the ubiquitous language with AI
- Implementation
- Increase efficiency from code completion to AI-driven implementation of complete features
- Context engineering to expand the range of tasks that can be implemented by AI agents
- Vibe coding vs. writing productive code with AI agents
- Connecting documentation and developer tools via MCP
- Creating fine-grained, explanatory commits with AI agents
- Support for API integration and library usage
- Test-driven agentic development
- Increasing code quality through refactoring and uniform concepts
- Understanding and modernizing legacy code faster
- Multi-agent workflows for further efficiency gains
- Testing & quality assurance
- Retrospective development of a test suite using AI agents
- Generate automated tests: unit, integration, and end-to-end
- Creating synthetic test data and identifying edge cases
- AI-supported reviews of code quality, security, and compliance with requirements
- Use of autonomous AI agents to improve quality
- CI/CD
- Generating CI/CD pipelines using AI agents
- Automated creation of release notes and changelogs
- Generation of infrastructure as code with AI agents
- Integration of AI-generated scripts into pipelines
- Setting up self-healing pipelines and automated troubleshooting using AI
- Operation & Monitoring
- Prioritization and assessment of alerts
- Automated bug fixing and ticket management
- Accelerating incident management through AI-based root cause analysis
- Integration of AI agents with observability stacks
- Formulating queries in observability tools
- Setting up dashboards and metrics with AI agents
Audience
Software developers, architects, enabling teams
Training Objectives
You can integrate generative AI into your software development process in a targeted manner and understand the added value it offers at every stage.
You will be able to analyze, structure and implement requirements more efficiently.
You can use generative AI to optimize architecture decisions and make them faster.
You are proficient in the use of AI tools to speed up the implementation and maintenance of your code.
You recognize and address risks and ethical challenges in the application of generative AI.
Your Trainers
Marco Steinke
INNOQ
Software architecture, AI
- Agentic Software Engineering
Marco Steinke is a consultant at INNOQ. His focus is on software architecture. He also deals with artificial intelligence, particularly the architecture and integration of AI systems.
Ole Wendland
INNOQ
Sustainable, future-oriented architecture; LLMs
- Agentic Software Engineering
Ole is a Senior Consultant and Software Architect at INNOQ in Switzerland. With his broad experience in software projects, he combines technical expertise with a deep understanding of the challenges faced by modern enterprises. His focus is on translating business requirements into sustainable, future-oriented solutions. As an all-rounder, Ole feels at home across the entire stack and continuously expands his spectrum of competencies. Along with his solid backend and frontend experience, he is deeply involved with Large Language Models (LLMs) and innovative applications of Foundation Models. Ole sees great potential in these technologies to optimize business processes and unlock new value creation opportunities for clients.
Roman Stranghöner
INNOQ
Conception and implementation of digital products
- Agentic Software Engineering
Roman works as a senior consultant and developer for INNOQ Germany. He builds web applications, preferably in agile teams and is focused on frontend related aspects like responsible use of web technologies, application architecture and tooling. His current interest lies in accessibility, responsive web design and user experience.
Our Customers Say
No sales pitch, just real learnings and practical experiences that have really helped us advance in the use of AI in the software development lifecycle.
Read in & go deeper
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Generative AI
The End of “Too Expensive” in Business Software? Exploring Features That Were Once Out of Reach. Go to blog post