Debjeet Bhowmik Imagine you need to solve a complex problem and you can hire one of two experts. The first is a brilliant “Improviser.” You give them the task, they immediately think of the very next logical step, execute it, see the result, and then figure out the step after that. They are fast and can adapt … Read more
Your shiny new weather agent, connected to a live API, works perfectly… until it doesn’t. A user asks for the weather in “Narnia.” The external API, quite correctly, returns a 404 Not Found error. Your Python script, not expecting this, throws an unhandled exception, and your entire agent crashes in a blaze of red error … Read more
Our agents have learned to think, plan, and use mock tools that exist only in our code. To be truly useful, however, they need to connect to the real, live, ever-changing world. They need to access data that isn’t in their training set—data that was generated just a second ago. Today, we break our agent … Read more
Your agent’s toolkit is growing. You’ve equipped it with a super-fast database_search for internal product specifications and a general-purpose web_search for everything else. A user asks, “What are the specs for our new product, the T-800?” The agent, instead of using the cheap, precise database tool, initiates a full web search. It finds the answer, … Read more
In our last post, we cured our agent’s amnesia with ConversationBufferMemory. This was a huge step forward, but it introduced a new problem. Our agent’s memory is now too perfect. It’s like creating a full, unedited transcript of a meeting. For a short meeting, this is fine. For a three-hour meeting, it becomes an unmanageable, … Read more
You’ve just built your first agent. You proudly run it and have a great first interaction: You: “My name is Rohan, and I’m interested in local history.” Agent: “Hello Rohan! It’s nice to meet you. Sodepur has a rich history. How can I help?” This is fantastic! You ask a follow-up question: You: “What did … Read more
For 29 posts, we’ve gathered our tools and learned the theory. We’ve meticulously explored the components of Agentic AI, mastering them one by one. Today, we assemble them. Today, we build our first intelligent agent. This capstone post will guide you through combining everything we’ve learned into a single, functioning application. We will build a … Read more
An agent’s true power isn’t just in its ability to reason; it’s in its ability to act. So far, our chains have primarily reasoned about text. Now, we are going to give our agent hands, eyes, and ears by connecting it to our own custom Python functions. While LangChain offers many pre-built tools for things … Read more
We’ve learned how to craft perfect prompts and pipe them into powerful chat models. But the model’s raw output is still just a ChatMessage object—essentially a block of text with a “role.” To build applications that can take programmatic action, we need to convert that text into a reliable, structured format like a JSON object … Read more
In the world of communication, sometimes you need to send a formal, one-way memo, and other times you need a dynamic, back-and-forth conversation. LLM providers offer models optimized for both scenarios, and LangChain gives us a clean, standardized way to interact with each. After mastering Prompt Templates, the next logical step in our chain is … Read more