Build an AI Agent That Never Forgets

Check out the Github notebook here →https://fandf.co/4syWH3o Try LangChain and Oracle AI Database 26ai → https://fandf.co/4syWH3o Your AI agent works perfectly… until the next session. Then it forgets everything. Not because your code failed. Not because the LLM is bad. But because AI agents are stateless by design. In this video, we build an AI agent from scratch that actually remembers across sessions, understands context semantically, and retrieves past knowledge without stitching together multiple systems. You’ll learn: • Why most AI agents forget • The hidden problem with memory layers • Why using 3 systems (cache + DB + vector DB) is fragile • The architecture shift that simplifies everything • How to build persistent memory using a single database • How semantic recall actually works in production We’ll implement a working agent using LangChain and Oracle AI Database 26ai, running locally with Docker. Resources: - Oracle Dev Hub: https://github.com/oracle-devrel/oracle-ai-developer-hub - System Design Course: https://academy.bytemonk.io/courses - ByteMonk Blog: https://blog.bytemonk.io/ - LinkedIn: https://www.linkedin.com/in/bytemonk/ - Github: https://github.com/bytemonk-academy Timestamps 00:00 The AI Agent Memory Problem 00:24 Building an Agent That Actually Remembers 00:34 Sponsor — Oracle AI Database 26AI 00:52 Why AI Agents Forget (Stateless LLMs Explained) 01:27 The Typical Memory Architecture (3-System Problem) 02:28 The Real Architectural Issue with Agent Memory 02:57 A Better Approach — Converged Database Concept 03:30 Running Oracle AI Database Locally with Docker 04:47 Project Setup & Environment Configuration 04:52 Building the Agent (Without Memory) 05:19 Demo — Agent That Forgets Everything 05:55 Adding Oracle as the Memory Layer 06:18 Demo — Persistent Memory Across Sessions 06:57 How Converged Memory Works (Meaning + Facts) 07:00 Code Walkthrough — Stateless Agent Architecture 07:40 Adding Memory with Oracle + LangChain 08:19 Designing