Agent Memory: Building Memory-Aware Agents

Introducing Agent Memory: Building Memory-Aware Agents, a short course built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez.

Most agents work well within a single session but lose everything the moment it ends. Memory engineering treats long-term memory as first-class infrastructure: external to the model, persistent, and structured. In this course, you’ll learn how to build that infrastructure using Oracle AI Database, LangChain, and LLM-powered pipelines.

You’ll design a complete memory system that stores and retrieves different memory types, scales tool access using semantic search, and builds write-back loops that allow agents to update their own memory autonomously. By the end, you’ll have assembled a fully stateful Memory Aware Agent that loads prior context at startup, assembles relevant context, state, tools, and outputs and improves across sessions.

In detail, you’ll:

• Explore why stateless agents fail at long-horizon tasks and learn the memory-first architecture, including the agent stack and memory core
• Build persistent memory stores for different agent memory types and implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory during execution
• Treat tools as procedural memory stored in a memory-backed store, and retrieve only the relevant ones at inference time using semantic search, solving the scaling problem of agents with hundreds of tools
• Build pipelines that extract structured facts from conversations, consolidate episodic memory into semantic memory, and create write-back loops that let your agent update and refine its own memory autonomously
• Assemble a fully stateful Memory Aware Agent with a startup routine that loads prior context, a recursive reasoning loop and persistence across sessions

Going beyond single-session interactions requires the right memory infrastructure, and this course gives you the hands-on patterns to build agents that don’t just respond, they remember and improve.

Developers building AI agents who want to go beyond single-session interactions. Familiarity with Python and basic LLM concepts is recommended.