Agent P

About Agent-P: Your AI Academic Ally

Inspiration

As educators ourselves, we've witnessed firsthand the overwhelming administrative burden that professors face daily. The inspiration for Agent-P came from a simple question: What if we could give educators more time to focus on what truly matters - their students and research?

What We Learned

Developing Agent-P has been an incredible learning journey. We've delved deep into:

Leveraging fetch.ai's agent framework for building autonomous AI agents
Utilizing Groq's advanced language models for natural language understanding and generation
Implementing multiagent architectures for complex task management
The intricacies of academic workflows and how to optimize them using AI

How We Built Agent-P

Agent-P is a sophisticated multiagent system built using fetch.ai's agent framework and powered by Groq's language models. We used Python as our primary programming language to develop and integrate the various components. Our system consists of four core agents:

Email Management Agent: The heart of Agent-P. Built with fetch.ai, this agent monitors the professor's .edu email, uses Groq to summarize important communications, and sends daily digest emails to the professor's personal email for approvals on critical decisions.
Lecture Preparation Assistant: This fetch.ai agent analyzes presentation materials using Groq's language model, generating 5-10 potential student questions along with comprehensive answers, enhancing classroom engagement.
Research Progress Monitor: Leveraging fetch.ai's autonomous capabilities, this agent keeps track of research students' progress, proactively reaching out to identify roadblocks. It uses Groq to analyze responses and suggest ways the professor can provide support.
Grading Assistant: Another fetch.ai agent that handles both multiple-choice and descriptive answers. For MCQs, it compares against answer keys. For descriptive responses, it leverages Groq's advanced language understanding to evaluate comprehension and assign scores. It also provides statistical insights like mean scores and identifies commonly missed questions.

Our multiagent architecture allows these agents to work in concert, sharing information and coordinating tasks to provide comprehensive support for professors. We integrated these agents with educational platforms like Canvas, showcasing the versatility of the fetch.ai framework in real-world applications.

Challenges We Faced

Our journey wasn't without hurdles:

Data Privacy: Ensuring the security and privacy of sensitive academic information while working with cloud-based AI models.
Scalability: Designing a multiagent system that could handle the diverse needs of different academic disciplines and institutions.
Accuracy in Grading: Fine-tuning Groq's language model to grade subjective answers fairly and consistently was particularly challenging.
Agent Coordination: Implementing effective communication and task allocation between multiple fetch.ai agents.

Despite these challenges, we're proud of what Agent-P has become - a cutting-edge, AI-driven tool that empowers educators to reclaim their time and focus on inspiring the next generation of thinkers and innovators.