During their time in college, students often struggle to juggle various responsibilities, ranging from coursework and college and job applications to academic planning, extracurricular activities, and more. Academic advisors work together with students to manage these responsibilities; however, their knowledge bases do not fully overlap. It can be difficult to provide specialized advice when an advisor has many students to keep track of. In this project, we attempted to answer the following question: To what extent can we bridge this gap using an agentic AI workflow that emphasizes personal context to deliver specific, targeted advice? 

We designed an agentic workflow utilizing the LangGraph framework to orchestrate communication between LLM agents and provide them with access to useful tools. Through a web application, users can interact with chatbots and input personal details. The LLM interfaces with a SQLite database that stores important academic information, as well as chat logs to retain personal goals and contextual information. This information is shared between students and their advisors to enable effective communication. 

We expect this program to have a marked influence on both student and advisor workloads by making crucial information more readily accessible, allowing them to make decisions more confidently and efficiently. We conclude by discussing how AI systems can be used to enhance, rather than replace, meaningful human interaction.