Introduction Imagine an AI that doesn’t just guess the answer but thinks through problems step by step, searches for information when it doesn’t know something, and adjusts its approach based on what it finds. This is the essence of the ReAct pattern – a revolutionary prompting paradigm that has become the foundation of modern AI agents . Developed…
Introduction Large language models like ChatGPT are impressive. They can write essays, answer questions, and generate code. But they have a fundamental limitation: they only know what they were trained on. Ask a question about your company’s internal documents, your customer data, or the latest news from yesterday, and they will either admit ignorance or—worse—hallucinate…
1) Executive Summary: Why Quantization Matters for Edge Agents The promise of edge AI is compelling: intelligent agents that run directly on devices—smartphones, IoT sensors, industrial controllers, and embedded systems—without relying on cloud connectivity. But there’s a fundamental tension: the most capable AI models are large, requiring significant memory and compute, while edge devices have…