My AI Agents Learning Journey: From Concepts to Real Applications

My Learning Journey with AI Agents Google AI Agents Writing Challenge Submission My Learning Journey / Project Overview Learning about AI agents was a completely new experience for me. Before taking this course, I only had a basic idea of how AI models work. But now I understand how AI can act, plan, reason, and interact with tools — just like a real assistant. During this journey, I learned: How AI agents break down a task into smaller steps. How they use tools like search, code execution, and memory. How agents communicate with APIs, handle tasks, and give structured results. How to design prompts that make agents more accurate and reliable. My project idea focuses on designing a Practical Exam Assistant Agent that helps students practise programming by generating questions, checking code, and giving feedback — similar to an online judge system. --- Key Concepts / Technical Deep Dive Here are the concepts that had the biggest impact on me: 1. Agent Architecture I learned how an agent has three main parts: Planner → Understands the task and makes a plan. Executor → Follows the steps one by one. Tool Handler → Uses external tools like search, coding environments, or APIs. This structure helps the agent stay organised and predictable. 2. Tool Use & Function Calling AI agents can connect with: APIs Databases Web search Code execution I learned how an agent decides when to call a tool and how to return results in a clean, structured format. 3. Memory & Context Management The idea that agents can remember long-term user information was very interesting. I understood how: Short-term context handles the conversation. Long-term memory stores stable details about the user. 4. My Capstone Approach For my project, the AI agent works like this: 1. Student selects a programming language. 2. Agent generates a practical question (like a coding exam). 3. Student submits code. 4. Agent evaluates using a code execution tool. 5. It returns: Output Errors Suggestions Time/space efficiency feedback This can help students prepare for labs and exams more effectively. ---
Reflections & Takeaways This course changed the way I look at AI. Earlier, I thought AI was only about giving answers. But now I understand AI can also take actions and complete tasks like a real assistant. My biggest takeaways: The quality of the agent depends on the clarity of the prompt. Tools make AI more powerful than simple chat models. Planning and reasoning are the core of reliable agents. AI agents can solve real problems in education, healthcare, business, and daily life. How I will use this knowledge: Build AI-powered features in my MERN stack projects. Create educational agents for coding practice. Explore automation tools for everyday tasks. Improve my resume-builder and practical portal using agent concepts. ---