A Cup Of Code

This is fully written by me without any help from AI, except for learning the concepts. This blog covers everything everyone should be aware of in the AI world, and it took me weeks to learn it so I could make it easy for you. To understand the basics of agents and how to build one, check out Part 1 of the Agentic AI series here .  Overview In Part 1, we read about the following: What are LLMs and how do they work How LLMs transform into agents to do real-world work Limitations of LLMs and AI Agents In this blog , we will be targeting one of the major limitations of LLM-based agents - memory, persistence, and checkpointing. LLMs are stateless, so they don't remember their past state and chats whenever they are called(through APIs), yet tools like OpenAI ChatGPT, Claude Code, Codex, etc. remember: The whole chat context from the very first message to the current message, even after days or weeks History of all the threads you have worked on with the AI tool The personalized facts ab...

This is fully written by me without any help from AI, except for learning the concepts. This blog consists of all the things that everyone should be aware of in the AI world, which took me weeks to learn to make it easy for you. Overview In the world of AI, LLMs are not sufficient. LLMs are simple(and yet complex) until you convert them to agents. This is where all the powers are unlocked, and LLMs can be used to do anything and everything in the world. This blog explains how a chatbot(or simple LLM) is actually converted into an AI Agent to do real-world, productive things. I'll explain from the basics what LLMs are, how they should be used, and how agents are created, but on a high level: LLMs(or large language models) are models trained(like Gemini, Opus, etc) on a huge amount of data and can be considered as "a brain in a glass jar" that can dictate what to do. Agents(or wrappers) are tools written which used LLMs internally to do real-world tasks. Chatbot - a common...