How do you solve a problem you don’t know the answer to? Think about a simple question: “What was the first movie directed by the person who won the Best Actress Oscar in 1998?” You don’t just guess. Your internal monologue probably goes something like this: This iterative loop of thinking, taking an action, and … Read more
You’ve set up your professional environment, learned the core concepts of secure and efficient coding, and you’re ready to build your first AI agent. Now you face a crucial decision: which toolkit should you use? The agentic AI space has exploded with frameworks, each offering a different approach to building intelligent systems. Choosing a framework … Read more
Imagine trying to diagnose a patient’s illness with no tools. No stethoscope, no X-ray, no blood tests. All you can see is the final symptom—a cough. You have no idea what’s happening internally to cause it. This is what it’s like to debug an AI agent without logging. An agent’s decision-making process, driven by a … Read more
We have our professional environment set up and our security protocols in place. We are on the verge of making our agent communicate with the outside world. But before we do, we need to talk about one of the most important concepts for building high-performance agents: waiting. An AI agent’s life is full of waiting. … Read more
So far, we’ve built a professional workshop for our AI projects. We have our clean environment, our version control, and our powerful code editor. Before we can connect to a Large Language Model and start building, we must address the most critical topic in modern development: security. Hardcoding an API key directly into your script … Read more
In our last post, we set up a professional development environment. One of the steps was to create and activate something called a “virtual environment.” We mentioned it was a “golden rule,” but its importance is so fundamental that it deserves its own dedicated guide. To understand why, imagine you have just one toolbox for … Read more
For the past ten posts, we’ve explored the fascinating concepts behind Agentic AI. We’ve moved from high-level ideas to the practical constraints of tokens and APIs. Now, it’s time to get our hands dirty. To build, we need a workshop. A master chef needs a clean, organized kitchen—a mise en place—before they can cook. A … Read more
In our journey so far, we’ve explored the incredible capabilities of AI agents—designing their personalities, crafting advanced prompts, and making them “think” methodically. We’ve been designing a high-performance race car. Now, it’s time to talk about the realities of driving it: the size of the fuel tank, the rate of fuel consumption, and, most importantly, … Read more
We’ve learned how to give an AI agent clear instructions and how to define its core personality with a system prompt. But what happens when the task isn’t straightforward? How do we handle problems that require logic, reasoning, and multiple steps to solve? To build agents that can tackle complex challenges, we need to move … Read more
In our last post, we explored the art of crafting a good prompt. But in an agentic system, not all prompts are created equal. To build a truly robust and reliable AI agent, we need to understand the critical difference between two types of instructions: User Prompts and System Prompts. Think of it like managing … Read more