This is a submission for the Agent.ai Challenge: Assembly of Agents (See Details)

What I Built

I created a Study Guide Agent to make learning smarter, easier, and more engaging. Users can input a topic, choose their education level or purpose (exams, academics, research), and get a detailed explanation tailored to their needs. To help retain information, the agent generates fun stories, and learning tips customized for better understanding and memory.

It doesn’t stop there 😁! Users can create a custom quiz to test their knowledge. The agent assesses their answers, providing personalized feedback, model answers, and actionable learning suggestions for improvement. This all-in-one guide is designed to simplify learning and make it enjoyable!

I built this because I had long wanted to, and this is closely related to a project I built this December as well:- AI-Study-Guide

Demo

My agent is live at:- Study Guide

A video demo showcasing my working agent:-

Agent.ai Experience

🎉 So, here it is— my 3rd agent on the agent.ai platform! 🎉

This one took a lot more planning compared to my first, but guess what? It’s totally worth it! ☺️😁

The process boils down to these key steps:

1️⃣ Start with the basics: Collect the user's education level and the topic they want to learn or revise.

2️⃣ Generate detailed explanations: Use an LLM agent to create a comprehensive, well-structured explanation of the topic tailored to the user’s needs.

3️⃣ Add a storytelling twist: Ask users if they’d like a story to make the topic memorable.

4️⃣ Bring in the Story Maker: Call the Story Maker Agent to craft a fun, engaging story based on the topic to help with retention.

5️⃣ Boost learning: Offer learning tips to simplify and reinforce the topic.

6️⃣ Create custom learning aids: Use an LLM agent to generate some learning tips, ensuring they’re tailored for the user as per the topic.

7️⃣ Quiz Questions Count: Let users pick how many quiz questions they’d like to assess their grasp of the topic.

8️⃣ Quiz time: Use an LLM agent to create a subjective quiz and present it to the user.

9️⃣ User input: Provide a text area for users to type their answers for each question based on their understanding.

🔟 Feedback and growth: Call an LLM agent to evaluate their answers, provide personalized feedback, model answers, as well as actionable suggestions for improvement.

🔢 Final output: Present everything back to the user— feedback, suggestions, and model answers— so they can deepen their understanding and grow in their learning journey.

Here’s a sneak peek of the actions tab of my agent, showing the workflow behind it all: 👇

Conclusion

This marks my third project on the Agent.ai platform, my second requiring interaction with another agent, and the most extensive one in terms of planning and execution. While I acknowledge it’s not the most time-efficient— since all content is generated afresh every time the agent is run, even if users don’t need the story or tips— I still like it, for now. I plan to refine and optimize it post my semester exams (because, let’s face it, there’s so much to do and barely a week left). Stay tuned for updates! 🚀

So, that's it!

If you're still here, Thank you ☺️🙏🏻.

Please share your thoughts, feedback, and suggestions in the comment section below 🗨️