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Picture this: You’re building a real-time LLM-powered app. Your users are expecting fast, continuous updates from the AI, but instead, they’re staring at a frozen screen. What gives? Perhaps surprisingly — it’s probably not your LLM that’s slowing things down. It’s your function calls . Every time your app makes a call to process data, hit an API, or load a large file, you risk blocking the stream. The result? Delays, lag, and an experience that feels anything but “real-time.”

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.