Master Thesis

Current AI agents in companies rarely keep a structured memory of past interactions, but imagine if they did Personalized, context-aware agents could adapt to user preferences in real-time: a user changing roles might want different recommendations, or prefer more detailed answers than before.

In this thesis, you'll explore memory-enabled multi-agent LLMs. You'll work with a central agent managing memory, supported by secondary agents performing RAG on ELCA's knowledge base. You'll investigate challenges like dynamic memory adaptation, user privacy, and control over stored information, all in a local, secure setup.

The goal: demonstrate practical value and show that a memory-driven agent can improve real-world business applications. You'll gain hands-on experience in a cutting-edge research area, helping expand conversational AI capabilities using proprietary data and multi-agent architectures.

Objectives

• Explore and deploy LLM memory ...