X @Avi Chawla

AI Agent Evolution - The industry has progressed from simple LLMs to sophisticated Agentic systems with reasoning, memory, and tool use [1] - Early transformer-based chatbots were limited by small context windows, exemplified by ChatGPT's initial 4k token limit [1] - The industry has seen upgrades to handle thousands of tokens, enabling parsing of larger documents and longer conversations [1] - Retrieval-Augmented Generation (RAG) provided access to fresh and external data, enhancing LLM outputs [1] - Multimodal LLMs can process multiple data types (text, images, audio), with memory introducing persistence across interactions [1] Key Components of Advanced AI Agents - Advanced AI Agents are equipped with short-term, long-term, and episodic memory [1] - Tool calling (search, APIs, actions) is a crucial feature of modern AI Agents [1] - Reasoning and ReAct-based decision-making are integral to the current AI Agent era [1]