Build Memory-Aware Agents

2026-03-18 · Source: DeepLearningAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

A new course, "Memory-Aware Agents," developed in partnership with Oracle and taught by Richmond Alak and Nacho Martinez, focuses on transforming stateless large language models (LLMs) into agents capable of learning and adapting over extended periods. The curriculum emphasizes architecting robust memory systems, a critical and debated topic in AI, moving beyond traditional prompt and context engineering. Participants will learn to design a complete agent memory system using the Oracle AI database, including developing a memory manager for abstracting operations and a semantic retrieval system. The course also covers building cognitive operations that enable agents to autonomously update and refine their memories, culminating in the creation of a fully memory-aware agent.

Key takeaway

For AI Engineers and Architects designing long-running AI agents, this course offers a structured approach to memory engineering. You should consider adopting a memory-first approach to enable agents to learn and adapt over days or weeks, moving beyond single-call prompt engineering. This will allow your agents to tackle more complex, long-horizon tasks effectively.

Key insights

Memory systems are crucial for transforming stateless LLMs into adaptive, long-horizon agents.

Principles

• Effective memory enables agents to learn.
• Memory engineering extends prompt engineering.

Method

Build an agent memory system using Oracle AI database, including a memory manager, semantic retrieval, and cognitive operations for autonomous memory updates.

In practice

• Design a memory manager.
• Implement semantic retrieval.
• Develop cognitive memory updates.

Topics

• Agent Memory
• LLM Agents
• Memory Engineering
• Oracle AI Database
• Semantic Retrieval

Best for: AI Engineer, Machine Learning Engineer, AI Architect

Related on AIssential

• 30 FREE Tutorials to Build AI Agents With Real Memory Fast! — The repository provides a practical, comprehensive guide to agent memory techniques, from basic to production-ready systems.
• Memory Systems for Long-Running Agents: Episodic to Procedural — Long-running agents require structured, retrieval-based memory systems to overcome context window limitations and scale economically.
• From Data Models to Mind Models: Designing AI Memory at Scale — Agentic memory enables AI systems to learn, adapt, and retain knowledge, crucial for complex, stateful operations.
• Your Agent Forgot Everything Again. Here’s Why That’s a Design Problem. — LLM agents lack episodic memory, hindering their ability to learn from past interactions and maintain context.
• Your AI Agent Has a Memory Problem — AI agents require robust memory systems to retain context and improve performance across sessions.
• Engineering Memory for AI Agents: A Practical Beginner’s Guide — AI agent memory selectively retains past interaction details to enable contextual, multi-step task execution.

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Editorial summary, takeaway, and curation by AIssential. Original article published by DeepLearningAI.