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The dream of an autonomous AI agent isn’t just about generating smart responses — it’s about making those responses fast, interactive, and context-aware. To achieve this, you need to manage state across asynchronous tasks, handle real-time communication, and separate logic cleanly. In this blog, you’ll learn how to design an ideal AI agent by: By the end, you’ll have a step-by-step understanding of how to design an agent that’s efficient, elegant, and easy to scale.

What if your AI agent could recognize its own mistakes, learn from them, and try again — without human intervention? Welcome to the world of self-correcting AI agents . Most AI models generate outputs in a single attempt. But self-correcting agents go further. They can identify when an error occurs, analyze the cause, and apply a fix — all in real time. Think of it as an AI with a built-in "trial and error" mindset. In this blog, you’ll learn:

Imagine having an AI agent that can access real-time weather data, process complex calculations, and improve itself after making a mistake — all without human intervention. Sounds kinda neat, right? Well, it’s not as hard to build as you might think. Large Language Models (LLMs) like GPT-4 are impressive, but they have limits. Out-of-the-box, they can't access live data or perform calculations that require real-time inputs. But with dynamic tooling , you can break these limits, allowing agents to fetch live information, make decisions, and even self-correct when things go wrong. In this guide, we’ll walk you through how to build an AI agent that can:

Real-time AI is transforming how users experience applications. Gone are the days when users had to wait for entire responses to load. Instead, modern apps stream data in chunks. For developers, this shift isn't just a "nice-to-have" — it's essential. Chatbots, search engines, and AI-powered customer support apps are now expected to integrate streaming LLM (Large Language Model) responses. But how do you actually build one? This guide walks you through the process, step-by-step, using FastAPI , Transformers , and a healthy dose of asynchronous programming . By the end, you'll have a working streaming endpoint capable of serving LLM-generated text in real-time.

LangChain and FastAPI working in tandem provide a strong setup for the asynchronous streaming endpoints that LLM-integrated applications need. Modern chat applications live or die by how effectively they handle live data streams and how quickly they can respond. LangChain is a library that simplifies the incorporation of language models into applications. It provides an abstracted layer over various components such as large language models (LLMs), data retrievers, and vector storage solutions. This abstraction allows developers to integrate and switch between different backend providers or technologies seamlessly. FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed for creating RESTful APIs quickly and efficiently, with automatic interactive API documentation provided by Swagger and ReDoc.