GenAI and Agentic AI for Software Engineering
Comprehensive hands-on program on integrating AI agents into software development. Learn to build software faster, more stable, and more scalable by using AI as part of the SDLC, not just as a tool.
overview
AI in software development is no longer an experiment but a new engineering standard that is already changing how we code, review, test and architect systems. AI will not take your job. But engineers who do not integrate the technology into their process will gradually lose their competitive edge.
This program is about the next step in the evolution of software engineering — towards AI-assisted and agent-driven development. You will learn to build reproducible processes, work with specifications and agents, and design systems that are integrated into the SDLC. As a result, you will start thinking like an AI-augmented engineer, and the speed and quality of your projects will grow regardless of the domain.
what you will learn
requirements for participants
Program
Workshop 1: Introduction and “Hello World” with AI
- Introduction to AI coding assistants (GitHub Copilot, Claude Code, Cursor)
- Comparison of the tools and their use cases
- Setting up the development environment
- Building a simple REST API with AI
- Comparing CLI- and IDE-oriented workflows
- the GitHub Copilot (CLI) workflow
- the Claude Code workflow
- a comparison of the approaches and the results
Workshop 2: Fundamentals — the Context, Model, Prompt framework
- The Context-Model-Prompt triad
- Best practices for context management
- Choosing models for different types of task
- Prompt engineering fundamentals
- Common mistakes and how to avoid them
- works with several formats (CSV, JSON, XML)
- detects potential fraud patterns
- demonstrates correct context management
- is tested with different models (small vs reasoning models)
Workshop 3: Specification-Driven Development
- Principles of specification-driven development
- The structure of a specification template
- Agent configuration files (agents.md, cursorrules, claude.md)
- The API-first approach
- Contract testing and validation
- review and analyse the specification document
- create an agent configuration file
- implement the API with AI on the basis of the specification
- verify the implementation against the specification
Workshop 4: Benchmarking and security, Skills
- Industry performance benchmarks (SWE-bench, GitHub Copilot leaderboards)
- Model selection strategies
- Cost versus quality trade-offs
- Reasoning models vs fast models
- Performance metrics in real-world scenarios
- Security of AI usage
- Adding skills to AI agents
- Review benchmark results and leaderboards
- Test the same task with different models
- Compare the results and the performance
- Analyse the impact of model usage costs
Workshop 5: AI Developer Workflows (ADW) — multi-agent systems
- The concept of AI Developer Workflows (ADW)
- The Architect–Editor pattern
- Orchestrating multi-agent systems
- Types of agent: tester, security auditor, documentation generator and others
- Integration patterns (side-car, in-process integration, API, service)
- Analyse the code of a fraud detection application
- Choose 2–3 agents to build
- Write the agent specifications
- Justify your choice of agents
- Implement the agents you chose
- Integrate the agents with the fraud detection system
- Demonstrate the interaction between the agents
- Extend the functionality of the system with agents
Workshop 6: PR review and automation
- AI-assisted PR review workflows
- Tool comparison: Cursor vs Claude Code vs Codex in code review
- CI/CD integration strategies
- Set up an automated code review system
- Compare the results and the recommendations of the tools
- Write substantive review comments
Workshop 7: Model Context Protocol (MCP)
- MCP architecture and its benefits
- Official MCP servers (Filesystem, Git, Memory, AWS and others)
- Enterprise MCP servers (GitHub, Azure, PostgreSQL and others)
- Orchestrating multiple servers
- Building your own MCP servers
- Set up several MCP servers (GitHub, AWS, PostgreSQL and others)
- Build a data assistant on top of MCP
- Create workflows that use several servers
- Demonstrate orchestration across servers
Capstone Project presentations
Project description
Applying the knowledge, course materials, tools and frameworks they have acquired, students build a project of their own. The work involves creating a full-fledged distributed system of interconnected AI agents that handle tasks within a complete pipeline. Teams can use the suggested project template or choose their own topic according to their interests.
Successfully preparing and defending the final project is a mandatory condition for receiving the course completion certificate.
Instructor
Oleksii Popov
Engineering Leader & Solution Architect with 16+ years of experience in scalable systems and AI-augmented productivity. Experienced in building technical products, leading cross-functional teams at scale, and translating business goals into scalable, secure, cloud-native solutions while implementing innovative technologies.
Benefits
Hands-on — workshops, assignments and cases with production logic
Tool-first rather than theory-first — working with real tools and protocols
A systematic path — from the basic principles to multi-agent systems in the SDLC
Expertise — learning from a practitioner in AI engineering productivity who shares what really works — and what does not
Practical artefacts — repeatable workflows, specifications, configurations and a final Capstone Project
who it is for
Software Engineers (Back/Full-Stack/SDET/Automation QA)
Tech Leads / development team managers
developers who want to grow and master the integration of AI into their workflows
Reviews
Volodymyr Zubchynskyi
Just wrapped up an incredible course on GenAI and Agentic AI for Software Engineering at SET University!
Instead of just using AI to help me write code, I’m now building the AI agents that handle complex development workflows autonomously.
We are moving way beyond simple autocomplete. Here is how the paradigm is shifting:
🔹 GenAI is the assistant — it waits for a prompt and helps you automate processes.
🔹 Agentic AI is the autonomous coworker — you define a high-level objective, and it orchestrates the necessary tools, runs automated tests, and manages the end-to-end implementation.
Moving from a “prompt engineer” to an “agent architect” is fundamentally redefining the software development lifecycle. 💻✨
Alyona Holovko
Happy to share that I’ve completed “GenAI & Agentic AI for Software Engineering” program at SET University!
The program covered a wide range of topics, from GenAI fundamentals to building Agents and Multi-Agent workflows.
I’m glad I can apply this knowledge in my day-to-day work, and I’m excited to keep exploring and learning.
Thank you so much to Oleksii Popov for the interesting and engaging lectures, for reviewing code, sharing your experience and giving valuable feedback. And thank you to SET University for this opportunity. 🙏
Andrii Hnatiuk
Before the course I mostly used AI as an assistant or a brainstorming tool. Afterwards, I see it much more as an engineering system that can — and should — be orchestrated to build other systems. What the program gave me was not so much knowledge of specific tools, which change extremely fast in the AI field, but a rethinking of approaches and general practices that stay relevant regardless of the particular stack. And those practices turned out to be strikingly similar to the ones software engineering has relied on for years — which strongly resonated with my own view.
Special thanks to Oleksii for the format: a constant dialogue with the audience, hands-on tasks, and the ability to adapt to the group’s pace.
I would also like to note the overall level of organization: everything is transparent, clear, and very well balanced for people who study while working. To me, that is exactly what a good engineering course looks like: what stays with you afterwards is not a set of specific recipes but approaches you can transfer to your own tasks.
Elena Chiperi
Finished GenAI and Agentic AI for Software Engineering. Showing what I built with it, rather than the certificate.
A pipeline that takes one ticket across several repositories. Eight agents that never talk to each other — not by rule, but because none of them holds a tool that could reach another. The only channel is files.
It takes nothing on trust — not the ticket, not its own work:
— every claim in the ticket is checked against the code before anything is written;
— an independent reviewer can reject the implementation, and a stronger model tier rewrites it;
— tests are written first, before the code.
On one ticket spanning two repositories that produced 765 lines of tests — four of those files were the first tests that repository had ever had.
And I measured it against a plain single session, seven times, writing down what I expected before each run. So I know not only that it works, but when not to reach for it.
Thank you, SET University — this is knowledge I will be using, all of it.
FAQ
Can I apply for the program if I do not know Python / JS?
Yes. The main focus of the course is on tools, workflows and principles (Context → Model → Prompt, specifications, MCP, agents) that are independent of any specific programming language. Python/JavaScript are used only for demonstration examples.
Learn more about the SET University program
