Agent Memory: Building Memory-Aware Agents
by DeepLearning.AI (with Oracle)
Build the memory layer as real infrastructure: typed stores, semantic tool retrieval, and write-back loops the agent maintains itself.
Overview
Built with Oracle and taught by Richmond Alake, Oracle's Director of AI Developer Experience, alongside Principal Data Science Advocate Nacho Martinez, this 1 hour 57 minute course treats long-term memory as infrastructure that lives outside the model rather than as a longer prompt. It opens with an 18-minute lesson on why stateless agents fail at long-horizon and multi-session work, then builds a system across four coded lessons. 'Constructing The Memory Manager' (22 minutes) implements storage and retrieval across distinct memory types — Alake's framing, laid out at length in his April 2026 SuperDataScience interview, separates episodic timestamped interaction logs, semantic factual knowledge, procedural workflow instructions often held as Markdown, and the working memory that is functionally the context window itself. 'Scaling Agent Tool Use with Semantic Tool Memory' (17 minutes) addresses a failure mode teams hit early: once an agent has dozens of tools, putting every schema in the prompt degrades both accuracy and cost, so tools are retrieved semantically instead of enumerated. The longest lesson, at 23 minutes, covers memory operations — extraction from raw interactions, consolidation of redundant or conflicting entries, and write-back pipelines that let the agent revise its own store without a human curating it. A final 20-minute lesson assembles these pieces into a working memory-aware agent. The stack is Oracle AI Database for vector storage and retrieval plus LangChain, so expect a vendor-specific implementation of otherwise vendor-neutral patterns. Alake argues throughout for 'memory engineering' as a named discipline joining decades of database practice to agent design.
At a Glance
- Topic
- Agentic
- Level
- Intermediate
- Format
- Course
- Cost
- Freemium
- Duration
- 1h 57m, self-paced — 6 video lessons (4 with code) plus a graded quiz
- Provider
- DeepLearning.AI (with Oracle)
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Explain why stateless agents fail on long-horizon and multi-session tasks that span days
- ✓Distinguish episodic, semantic, procedural and working memory, and choose storage for each
- ✓Build a Memory Manager that routes writes and reads across multiple typed memory stores
- ✓Implement semantic tool retrieval so dozens of tools never flood the agent's context window
- ✓Design extraction pipelines that turn raw interaction transcripts into durable memory entries
- ✓Consolidate redundant and conflicting memories so the store stays useful as it grows
- ✓Build write-back loops letting an agent autonomously update what it knows between sessions
- ✓Select embeddings and indexing strategies for vector retrieval over a long-lived memory store
Highlights
- •Treats memory as a system to engineer — typed stores, retrieval, consolidation, write-back — rather than as a chat-history buffer
- •The semantic tool memory lesson solves a real scaling problem most tutorials never reach: what to do past a dozen tools
- •Four of six lessons are code, so you finish with a running memory-aware agent rather than a taxonomy
- •Taught by the practitioner who has been arguing publicly for 'memory engineering' as a discipline, including on the SuperDataScience podcast
- •Free to watch in full; only the graded quiz and completion badge sit behind a DeepLearning.AI PRO membership
Who It’s For
Best For
- ✓Engineers whose agents lose context between sessions and forget what a user already told them
- ✓Teams building customer-support copilots or assistants that must retain state across days
- ✓RAG practitioners extending retrieval from static documents to evolving agent-written memory
- ✓Developers hitting accuracy or cost problems from too many tool schemas in the prompt
Prerequisites
- •Python familiarity — enough to read and modify LangChain code in a notebook
- •Basic LLM concepts including context windows, embeddings and vector similarity search
- •Prior exposure to RAG or a vector database is helpful but not assumed
FAQ
What is Agent Memory: Building Memory-Aware Agents?
A DeepLearning.AI short course built with Oracle on treating agent memory as persistent infrastructure outside the model rather than a longer prompt. You build a Memory Manager handling distinct memory types, a semantic tool-retrieval system that stops tool schemas from flooding the context window, and extraction, consolidation and write-back pipelines that let an agent update what it knows without human curation.
Is Agent Memory: Building Memory-Aware Agents free?
Agent Memory: Building Memory-Aware Agents offers free content, with paid options for certificates or premium features.
What level is Agent Memory: Building Memory-Aware Agents for?
Agent Memory: Building Memory-Aware Agents is aimed at a intermediate audience. Recommended background: Python familiarity — enough to read and modify LangChain code in a notebook, Basic LLM concepts including context windows, embeddings and vector similarity search, Prior exposure to RAG or a vector database is helpful but not assumed.
How long does Agent Memory: Building Memory-Aware Agents take?
Expect roughly 1h 57m, self-paced — 6 video lessons (4 with code) plus a graded quiz. Most learners work through it at their own pace.
What will I learn from Agent Memory: Building Memory-Aware Agents?
You'll learn: Explain why stateless agents fail on long-horizon and multi-session tasks that span days; Distinguish episodic, semantic, procedural and working memory, and choose storage for each; Build a Memory Manager that routes writes and reads across multiple typed memory stores; Implement semantic tool retrieval so dozens of tools never flood the agent's context window; Design extraction pipelines that turn raw interaction transcripts into durable memory entries; Consolidate redundant and conflicting memories so the store stays useful as it grows; Build write-back loops letting an agent autonomously update what it knows between sessions; Select embeddings and indexing strategies for vector retrieval over a long-lived memory store.
Topics
Sources
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