Agentic Software Engineering

Generative AI is changing how we build software — from single code snippets to entire agent-driven development workflows. In our new three-day training course, Agentic Software Engineering, you’ll learn how to turn this transformation into actionable practices for your team.

Focus: Agentic Engineering

You’ll learn how to effectively use AI assistants and autonomous agents — from smart requirements analysis and automated architecture design to coding agents that independently implement complex features. Throughout the process, you stay in control and know where human expertise remains critical.

What’s in it for you

After the training, you’ll be able to assess which AI tools and approaches make sense for your specific challenges. You’ll know how to move from basic code completion to advanced, agent-based workflows — step by step.

Who is this for?

This training is designed for developers, architects, and tech-adjacent roles who want a systematic, hands-on approach to using generative AI across the entire software development lifecycle.

Agenda

1. introduction & context
   • Why foundation models and generative AI are shaping the future of software development
   • Technological change and its concrete impact on your day-to-day work
   • Clarify terms: LLMs, multimodal models, foundation models
2. Requirements with generative AI
   • AI-supported creation, analysis and refinement of requirements
   • Generating user stories from natural language and multimodal content
   • Automatically translate customer needs into structured requirements
   • AI-supported classification and prioritization of requirements
   • Create prototypes faster and effectively involve non-technical team members design architecture efficiently
3. Automated derivation of system architectures from requirements
   • Create software design proposals using generative AI
   • Reflecting and documenting architectural trade-offs
   • Agent-based simulation and evaluation of architecture variants
   • Accelerate implementation
4. Increase efficiency from code completion to AI-driven implementation of complete features
   • upport for API integration and library usage
   • Increase code quality through automated refactoring recommendations and documentation
   • Understand and modernize legacy code faster
5. Optimize testing & quality assurance
   • Generate tests automatically: Unit, integration and end-to-end
   • Create synthetic test data and identify edge cases
   • AI-supported reviews for test coverage and quality
6. Make CI/CD more efficient
   • Generate CI/CD configuration automatically (GitHub actions, GitLab pipelines)
   • Automated creation of release notes and changelogs
   • AI-based security scans and compliance checks
   • Self-healing pipelines and automated troubleshooting
7. Improve operation & monitoring
   • Accelerate incident management with AI-based root cause analyses
   • Automated monitoring and prioritization of alerts
   • AI-based log analysis and integration with observability stacks
8. Strengthen maintenance & further development
   • Automated bug fixing and ticket management
   • AI-based explanations of code changes (“Diff Explainers”)
   • Automatic detection of regressions and technical debt reduction

Interested?

If you have any questions about Agentic Software Engineering or our services, feel free to reach out to Robert Glaser, Head of Data & AI at INNOQ.