Microsoft Developer presents a deep dive into AI agent memory systems, featuring memory management strategies, self-improvement, and code samples using Mem0, Semantic Kernel, and Azure AI Search.

AI Agent Memory: Building Self-Improving Agents

This video lesson from Microsoft Developer offers developers a comprehensive overview of how memory management underpins self-improvement and personalization for AI agents. Key topics include:

Introduction

Learn why memory is crucial in the context of building effective, adaptive AI agents.

What is Memory for AI Agents?

A conceptual explanation defining memory in AI agents and its distinctions from traditional software state management.

Different Types of Memory

Understand various types of memory systems relevant for AI agents, such as short-term and long-term memory, episodic memory, and contextual information storage.

Methods for Storing Memory

Explore different methods and technologies for managing and persisting agent memory efficiently.

How Memory Enables Self-Improvement

See how strategic memory design helps AI agents learn from experience, improve over time, and tailor solutions for individual users.

Code Sample: Mem0 + Semantic Kernel + Azure AI Search

Get hands-on with practical code illustrating:

How Mem0 can be integrated to create and persist agent memories
Using Semantic Kernel for orchestrating memory recall and processing
Leveraging Azure AI Search for scalable memory storage and retrieval

Real examples demonstrate implementing these approaches using Microsoft technologies.

Wrapping Up

The lesson concludes with a recap of key takeaways and an invitation to explore the sample code provided.

Sample Code

Access sample code for the video at: http://aka.ms/ai-agents-for-beginners

Authored by Microsoft Developer, this session offers actionable technical advice and live demonstrations for those interested in building advanced AI agents with memory on Microsoft’s cloud AI stack.