Weekend Agent Challenge: Smart AI Daily Planner Agent
Built an autonomous AI-powered daily planner using Amazon Bedrock, AWS Lambda, Amazon Event Bridge Scheduler, and Amazon SES. Every morning, the agent generates a personalized study schedule and automatically delivers it via email.
Weekend Agent Challenge: Smart AI Daily Planner Agent
Vision
As a student, I often spend valuable time deciding what to study each day instead of actually studying. I wanted to build an AI-powered assistant that plans my day automatically so I can start working immediately.
The goal of this project was to create an autonomous AI agent that works without requiring any manual interaction. Instead of waiting for me to open an application or click a button, the agent wakes up on a predefined schedule, generates a personalized study plan using AI, and emails it directly to me.
Every morning, the agent creates a realistic study schedule that includes Python practice, AWS learning, aptitude preparation, college assignments, exercise, breaks, meals, and a motivational quote.
This saves time, improves productivity, and helps maintain a consistent daily routine.
What the Agent Does
The Smart AI Daily Planner Agent runs automatically every day using Amazon EventBridge Scheduler.
When triggered, the following steps happen automatically:
- Amazon EventBridge triggers an AWS Lambda function.
- Lambda collects my predefined learning goals from environment variables.
- Lambda sends a prompt to Amazon Bedrock using the Amazon Nova Lite foundation model.
- Bedrock generates a personalized study schedule for the current day.
- Lambda formats the response.
- Amazon SES sends the schedule directly to my email inbox.
The entire workflow is fully automated and requires no manual interaction.
Example output:
- 6:00 AM – Exercise
- 7:00 AM – Python Programming
- 9:00 AM – AWS Practice
- Afternoon – College Assignments
- Evening – Aptitude Preparation
- Motivational Quote of the Day
How I Built It
I designed the solution using AWS serverless services to keep the architecture simple, scalable, and cost-effective.
The development process started with creating an AWS Lambda function using Python. I then enabled Amazon Bedrock and connected the Lambda function to the Amazon Nova Lite model using the Bedrock Runtime API.
To make the planner flexible, I stored configuration values such as the student's name, study goals, sender email, receiver email, and model ID as Lambda environment variables instead of hardcoding them.
After successfully generating study plans with Bedrock, I integrated Amazon SES to automatically send the generated schedule via email.
Finally, I configured Amazon EventBridge Scheduler to invoke the Lambda function every morning, making the entire workflow autonomous.
One challenge I encountered was the default Lambda timeout. AI model inference sometimes exceeded the default execution time, so I increased the timeout and memory allocation to ensure reliable execution.
AWS Services Used
The solution uses four AWS services:
- Amazon Bedrock (Amazon Nova Lite) for AI-powered schedule generation
- AWS Lambda for serverless compute
- Amazon EventBridge Scheduler for automatic daily execution
- Amazon Simple Email Service (SES) for delivering the generated schedule via email
Architecture Overview
EventBridge Scheduler
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AWS Lambda
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Amazon Bedrock (Nova Lite)
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Generate Daily Study Plan
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Amazon SES
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Email Delivered
The workflow is completely automated and serverless.
What I Learned
This project helped me gain practical experience with multiple AWS AI and serverless services.
I learned how to:
- Build serverless applications with AWS Lambda
- Invoke Amazon Bedrock foundation models
- Design AI prompts for better responses
- Schedule workloads using Amazon EventBridge
- Send transactional emails using Amazon SES
- Manage configuration securely using Lambda Environment Variables
- Build autonomous AI agents that operate without user interaction
More importantly, I learned how different AWS services can be combined to build intelligent automation solutions with very little infrastructure.
Future Improvements
Some enhancements I plan to add include:
- Google Calendar integration
- Personalized plans based on weekdays and weekends
- Weather-aware scheduling
- Priority-based task management
- Voice notifications
- Mobile app integration
GitHub Repository
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Amazon EventBridge
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AWS Lambda
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Amazon Bedrock
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Generate Daily Planner
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Amazon SES
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📧 Email
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---# Final Checklist- ✅ Title- ✅ Description- ✅ Tags (`#agents` included)- ✅ 500+ words- ✅ Vision- ✅ What the agent does- ✅ How you built it- ✅ AWS services used- ✅ Architecture overview- ✅ What you learned- ✅ GitHub link placeholderThis article is ready for submission once you replace the GitHub placeholder with your repository URL and optionally add an architecture diagram or screenshots.
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