# @awomanindatascience on Instagram

- **Type:** Video
- **Original URL:** https://www.instagram.com/p/DYDxkMXz28K
- **Gondola URL:** https://gondola.cc/posts/65284733-awomanindatascience-instagram
- **Thumbnail:** https://img.gondola.cc/tr:w-,h-,fo-auto/postThumbnails/e22d26b1c8.jpg
- **Posted:** 2026-05-08T01:20:20.000+00:00
- **Account Owner:** Chinar Arora (@awomanindatascience) — https://gondola.cc/awomanindatascience

## Caption

Day 3 of my Agentic AI series — Memory in AI Agents 🧠

The biggest shift from “LLMs” → “agents” isn’t just tool calling. 
It’s continuity.

In the Stanford + Google generative agents paper, agents didn’t work because the model was smarter. They worked because the agents could remember, retrieve, and reflect.

Three layers of agent memory:
• Context window = CPU → what the agent is actively thinking about 
• KV store = RAM → user preferences + structured state 
• Vector DB = SSD → semantic long-term memory across sessions

And the most important part? Reflection.

Not just storing:
“John looked stressed today.”

But synthesizing:
“John has been under pressure lately.”

That’s the jump from logging → reasoning.

A good agent doesn’t just answer. 
It accumulates experience.

Memory is what turns interactions into learning. 
And continuity is what makes an agent feel alive.

#AgenticAI #LLM #AIEngineering #GenerativeAI #AIAgents MachineLearning ArtificialIntelligence RAG VectorDatabase AI

## Stats

- **Views:** 500
- **Likes:** 39
- **Shares:** 0
- **Comments:** 0

## Tags

generativeai, aiengineering, llm, agenticai, aiagents

---
Copyright (c) Gondola