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If you're a React developer, it's a safe bet you've encountered fetch waterfalls - also called request waterfalls or network waterfalls. Their distinctive shape crops up in analytics tools when you go try to see what's taking the page you painstakingly designed is taking so long to load. In this blog post, we’ll go into what fetch waterfalls are, why they happen, how they affect your React applications, and, most importantly, how to avoid them. Lets start with the basics.

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: