AI Agentic Memory for beginners.

Hello there, it's been long since I posted my last blog. I have recently been learning a lot about AI agents and I thought of writing a small blog on how AI agent memory works. This is a very beginner-friendly blog. Someone who has little to no idea about how/why AI agentic memory works can get a simple mental model to understand this, and it can become an entry point to explore further. Context, Context, Context TL;DR Memory is a context to LLM If you have been active in the world of LLMs, you probably have heard of this term "Context". It acts as an input to LLMs to produce a response. The context goes through the neural network and predicts the "token" as a response. The more specific the context is, the more high-quality output you will get. For example, if you ask a question like "How to fix a memory leak in my JavaScript code?". It may return a generic response like "ensure that you are freeing your memory after invoking the function". It can also give you a list of possible causes of memory leaks. It may or may not resonate with the specific problem you are dealing with. On the other hand, if you give code snippets, add some additional details such as how it's getting called, what parameters the functions are expecting, what it returns, and so on. The chances are you will get a personalised, high-quality response in a one-shot prompt. Sometimes context becomes the most important thing to LLMs to get a better outcome regardless of the LLM's intelligence. LLMs have a "Context Window" which basically represents a capacity to process a number of contexts when generating the next token. Although the context window is getting larger and larger, Gemini 3 Pro has up to a 1 Million Token context window. Since the core foundation of LLM is based on attention , it becomes harder for LLMs to consider and give the same attention to all the tokens when generating the next token as an output. An earlier research paper "Lost in the Middle: How Language Models Use Long Contex