AI agents are transforming how we build intelligent applications. In this tutorial, we'll build a study planner agent that uses Google's Gemini API to create personalized study schedules.
What We're Building
Our study planner agent will:
- Accept a list of subjects and available time slots
- Generate an optimized study schedule
- Adapt recommendations based on difficulty levels
- Provide spaced repetition reminders
Setting Up the Project
First, install the required dependencies:
pip install google-generativeai python-dotenv
Designing the Agent Architecture
The agent follows a simple but effective pattern:
- Input Parser: Processes user preferences
- Schedule Generator: Uses Gemini to create study plans
- Optimizer: Applies spaced repetition principles
- Output Formatter: Returns clean, actionable schedules
Implementing the Core Logic
The heart of our agent is the interaction with Gemini's API. We structure our prompts to leverage the model's understanding of learning science.
Key considerations:
- Context window: Include enough context about the subjects
- System prompts: Define the agent's persona as an educational expert
- Output format: Request structured JSON for easy parsing
Testing and Iteration
Always test with edge cases:
- What happens with only one subject?
- How does it handle very limited time?
- Does it properly space difficult subjects?
Conclusion
Building AI agents doesn't have to be complex. With the right API and a clear architecture, you can create powerful tools that genuinely help users.
