Implementation of a goal-oriented BDI agent in Java that navigates a maze using symbolic reasoning. The agent uses a simplified first-order logic (sfol) for beliefs, goals, and actions, with forward-chaining inference and iterative deepening planning. Developed as part of the Symbolic AI course (4032KI) at Leiden University.

A simple Spring Boot project using the Embabel framework with a REST API–based meeting summarizer agent that leverages GOAP planning and LLMs for extracting key insights and action items.

A Spring Boot Embabel demo focused on showcasing how @condition drives decision-making in an e-commerce replacement workflow. The agent checks conditions such as product stock availability: if a product is in stock, a replacement is issued; if the product is out of stock, a refund is initiated.

A Spring Boot Embabel Shell demo showcasing a meeting summarizer agent that uses GOAP planning and LLMs to extract key points and action items from transcripts.