Build an AI Study Planner Agent That Automates Tasks Using APIs
Picture an AI that not only turns a YouTube lecture into a quick summary, but also books study slots, pings you with reminders, and even hunts down practice questions. That's the direction the new wave of AI "agents" seems to be heading, moving past plain chatbots toward helpers that can actually act in the digital world for you. After a tutorial that showed how to build a basic YouTube summarizer, a recent paper walks through a more ambitious study-planner agent.
The twist? It talks to several APIs, those little bridges that let different programs exchange data. The original demo used just one tool; this version can juggle a few.
Imagine feeding it a syllabus, then watching it call a calendar API to reserve time and a task-manager API to add items to a to-do list. Switching from a single-purpose bot to a coordinator feels like a real step toward usable automation. It suggests we can program AI to stitch together multi-step workflows, almost like a personal project manager.
If it works as described, the tech could shift from merely fetching info to actually handling everyday tasks.
Here, we will build Agents that can automate tasks and interact with external tools and APIs. In the first article, we built a simple YouTube summarizer agent, where we were using just one tool (YouTubeTools). In this second article, we will take things a step further by building a Study Planner Agent that creates personalized study schedules based on user input and deadlines.
This agent automatically creates Tasks in Jira and sends calendar invites using Cal.com for easy tracking and execution. For the purpose of the tutorial, we will use Google Colab notebook to write and execute the code and Phidata Agentic AI Platform to power the Agent. By the end, you will see how AI Agents can move from being helpful assistants to becoming fully automated systems capable of streamlining complex, real-world workflows.
Note: This is the second article in a two-part series on building AI Agents from the ground up. In the first article, we explored the value of AI Agents, introduced popular Agentic AI platforms, and walked through a hands-on tutorial for building a simple AI Agent using Phidata.
Building this study-planner agent feels like a natural next step after our first YouTube summarizer, which only talked to one tool. Now the agent juggles a few services: it reads what you type, works out a rough timeline, then opens tickets in Jira. That move from a single-purpose bot to something that can stitch together a workflow hints at what digital helpers could become.
Right now it’s tuned for school schedules, but the core idea, an agent that understands a goal, splits it into doable pieces, and talks to different APIs, could probably be repurposed for things like software project tracking, content pipelines, or even a custom workout plan. The tricky part isn’t getting a plan on paper; it’s whether the agent will later reshape that plan when you hit a snag or your priorities shift. We’re still figuring out how to make that kind of back-and-forth feel smooth.
This guide gives you the basic building blocks to start experimenting with that next phase.
Resources
- AI-Based Study Planner - International Research Journal on Advanced Engineering Hub (IRJAEH)
- Mastering AI API Spreadsheet Automation in 2025 - Sparkco AI
- 12 Best AI Tools for Automated Research and Report Writing (2025) - Skywork AI
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Common Questions Answered
What external tools and APIs does the Study Planner Agent use to automate tasks?
The Study Planner Agent interacts with external tools and APIs, specifically creating tasks in Jira for project management and sending calendar invites using Cal.com. This automation allows the agent to handle scheduling and reminders directly within these external systems.
How does the Study Planner Agent differ from the initial YouTube summarizer agent built in the first article?
The Study Planner Agent represents a progression beyond the basic YouTube summarizer, which used only one tool (YouTubeTools). This new agent orchestrates multiple external systems to create personalized study schedules, moving from single-tool functionality to integrated workflow management.
What specific functions does the Study Planner Agent perform based on user input and deadlines?
Based on user input and deadlines, the Study Planner Agent creates personalized study schedules. It processes this information to calculate timelines and automatically generates tasks in Jira and calendar invites via Cal.com for easy tracking.
What does the development of this agent demonstrate about the progression of AI agents?
This study planner agent demonstrates a clear progression from isolated automation to integrated workflow management. It showcases how AI agents can orchestrate multiple external systems, representing a significant step toward more sophisticated practical applications.