L.079.05705

The teaching language will be English. Questions in German will be permitted.

Public course material is being made available on this page.

To attend the course, you have to register in the PAUL system as a participant.

To ask questions, please use the discussion forum in PANDA so that others can benefit from the answers as well.

If needed, we will also send updates through PANDA circulars.

This schedule gives a rough indication of which topics will be covered when.

The following course schedules are non-binding and may change at any time and without prior announcement.

Exercise session will be held on Monday every week. 

There will be 6 exercise sheets published in PANDA, which will be graded two at a time after the second deadline of each pair (sheets 1+2, 3+4, 5+6). 

Exercise sessions will introduce each newly released exercise sheet and discuss the results of the previous assignment sheet. 

They will also discuss labs. 

A tentative schedule will be uploaded soon.

Since the emergence of generative AI (GenAI), and particularly since the use of GenAI within agents, software engineering (SE) has been undergoing a radical transformation that raises many questions: In which phases of SE can GenAI be deployed effectively? What are the opportunities, but also the limitations and pitfalls involved? How does GenAI change the competency profile that will be expected of software engineers in the future? In this course, the use of GenAI and agents across the various phases of the software lifecycle (requirements analysis, implementation, testing, etc.) is discussed, made tangible through hands-on experience, and — where appropriate — critically examined. We will also cover current research on AI-driven software development methods. In practical exercises, students are introduced to current technologies that enable or simplify the design and deployment of agents in software engineering.

Final exam:

TBC

Topics covered include:

• Fundamentals of generative AI (concise recap as a starting point)
• Retrieval-Augmented Generation and agents: how each works and how they differ
• Economic perspective: productivity effects (empirical studies), cost, latency, and model economics
  in production agent deployment
• Knowledge and context engineering for SE tasks: providing repositories, issues, tests, and documentation as context (information curation, context windows, repo maps, RAG over
  codebases)
• Tool use and function calling: interfaces between agents and development tools (compiler, test runner, linter, debugger, version control), and standards such as the Model Context Protocol (MCP), Agent Client Protocol (ACP)
• Common types of agents (ReAct, Plan-and-Execute, Reflexion, etc.)
• Multi-agent systems (incl. Agent2Agent protocol)
• Benefits and challenges of agents across the various phases of the software lifecycle (requirements analysis, implementation, testing, etc.)
• Feedback through testing and symbolic methods (e.g., static analysis and verification) as a corrective and safety net
• Reliability and limitations: hallucinations, the out-of-distribution problem with proprietary codebases, correctness guarantees
• Security of agentic systems: prompt injection, autonomous execution, supply-chain risks from generated code
• Human-in-the-loop, accountability, and code ownership: the changing role and competency profiles of software engineers

After completing this module, students are able to

• explain how generative AI, retrieval-augmented generation, and agentic systems work and distinguish their respective areas of application in software engineering,  
• apply current agent technologies to tasks across the software lifecycle and provide them with suitable context through repositories, tests, and tool interfaces,
•  analyse the benefits and challenges of using GenAI and agents in the individual phases of software development,
• assess the reliability, security, and limitations of agentic systems, including hallucinations, the out-of-distribution problem, and correctness guarantees,
• design their own agent-based solution for a software engineering task and justify the architectural choices involved,
• evaluate the economic and organisational implications of deploying agents productively, including effects on cost, productivity, and the role of software engineers.