Agentic Software Engineering Training

Generative AI is changing how we build software—from single code snippets to full-blown, agent-driven development workflows. In our new three-day training, “Agentic Software Engineering,” we show you how to turn that shift into real outcomes for your team.

Focus: Agentic Engineering

You’ll learn how to apply AI assistants and autonomous agents across the entire engineering lifecycle—from intelligent requirements analysis and automated architecture design to autonomous code agents that implement complex features. You stay in control and understand where human expertise remains essential.

What you’ll achieve

After the training, you’ll be able to judge which AI tools and approaches fit your challenges and how to move step by step from simple code completion to advanced agent-based workflows.

Who is this for?

Developers, architects, and adjacent technical roles who want to systematically unlock practical uses of generative AI across the entire software development process.

Agenda

1. Intro & Context

• Why generative AI is shaping the future of software engineering
• Core AI concepts: tokens, autoregression, temperature, prompts, context, tools, Model Context Protocol (MCP), memory
• Agents vs. assistants
• Getting started with agentic ways of working

1. Requirements

• Create, analyze, and refine requirements with AI support
• Automatically turn customer needs into structured requirements
• Generate epics and user stories from natural language and multimodal inputs
• Ensure consistently structured requirements using few-shot prompts
• Build prototypes with agents—even without a technical background

1. Architecture

• Design system architectures with AI agents
• AI-assisted decision-making and documentation
• Derive quality attributes from requirements and stakeholder interviews
• Uncover architectural inconsistencies with AI agents
• Identify and document technical debt with AI
• Spot deviations from the ubiquitous language (Domain-Driven Design)

1. Implementation

• Boost efficiency from code completion to AI-driven implementation of complete features
• Use context engineering to expand the task packages agents can execute
• Write vibe coding vs. production code with AI agents (vibe coding = exploratory, freeform coding to discover approaches)
• Connect documentation and developer tools via MCP
• Create fine-grained, explanatory commits with AI agents
• Support API integrations and library usage
• Test-driven, agentic development
• Improve code quality through refactoring and consistent patterns
• Understand and modernize legacy code faster
• Orchestrate multi-agent workflows for further efficiency gains

1. Testing & Quality Assurance

• Build a test suite after the fact with AI agents
• Generate automated tests: unit, integration, and end-to-end
• Create synthetic test data and uncover edge cases
• Run AI-assisted reviews for code quality, security, and requirement coverage
• Apply autonomous AI agents to raise overall quality

1. CI/CD

• Generate CI/CD pipelines with AI agents
• Automate release notes and changelogs
• Produce Infrastructure as Code with AI agents
• Integrate AI-generated scripts into pipelines
• Set up self-healing pipelines and automated troubleshooting with AI

1. Operations & Monitoring

• Prioritize and assess alerts
• Automate bug fixing and ticket management
• Speed up incident management with AI-based root-cause analysis
• Integrate AI agents with your observability stack
• Write effective queries in observability tools
• Set up dashboards and metrics with AI agents

For questions about Agentic Software Engineering and our offering, feel free to reach out to Robert Glaser, Head of Data & AI at INNOQ.