Memory & Context Management
AGAI 203
Learn how AI agents store, retrieve, and manage information across interactions. Explore the different types of agent memory — in-context, episodic, semantic, and procedural — and the techniques used to give agents effective long-term recall.
The Memory Problem
Language models have a fundamental limitation: they process a fixed context window and then forget. Every new conversation starts from scratch. For most applications, this is a serious constraint. Real-world tasks require agents to remember past interactions, accumulate knowledge, track progress, and build on previous work.
Types of Memory
This course introduces the different types of memory an AI agent can have and how to implement each one. From the simplest approach — stuffing everything into the context window — to sophisticated retrieval-augmented architectures with vector databases, you will learn the trade-offs and practical implementation patterns.
What You Will Learn
You will implement in-context memory management strategies, build a basic RAG pipeline with a vector database, apply memory compression and summarization to extend effective context, and evaluate retrieval quality. You will understand the four categories of agent memory — in-context, episodic, semantic, and procedural — and when to use each. You will come away with practical skills for giving agents meaningful long-term recall.
Who This Course Is For
This course is for developers building agents that need to work with large knowledge bases, persist information across sessions, or recall past interactions. If your agent keeps forgetting context or cannot find relevant information reliably, this course addresses those problems at the architectural level. Basic familiarity with vector search concepts is helpful but not required.
What you will learn
- Describe the four types of agent memory and their uses
- Implement in-context memory management strategies
- Build a basic RAG pipeline with a vector database
- Apply memory compression to extend effective context
- Evaluate memory retrieval quality
- Choose appropriate memory strategies for different tasks
Major topics
Why this course matters
Memory is what separates a useful agent from a stateless responder. Agents that remember context, accumulate knowledge, and build on past interactions can tackle tasks that are otherwise impossible for AI systems.
Course modules
Context Window and Memory Foundations
Understand why context management is one of the central engineering problems in agentic AI. This module explains the context window, why it is not the same as memory, and how in-context, episodic, semantic, and procedural memory support different agent behaviors.
External Memory and RAG
Learn how agents use external memory systems to retrieve facts, documents, and past knowledge. This module explains embeddings, vector databases, semantic search, and how to build a retrieval-augmented generation pipeline from scratch.
Memory Management and Evaluation
Move beyond basic retrieval into production memory management. This module covers summarization, compression, retrieval quality metrics, reranking, freshness, privacy, and how to choose the right memory architecture for a given agent.
Common misconceptions
Increasing the context window solves the memory problem
Vector databases are the only way to give agents memory
Agents can remember everything they've ever processed
Memory retrieval is always accurate
Ask your AI guide
Ask anything about Memory & Context Management, or choose a suggested question below.
AI responses are educational and may not be perfectly accurate. Press Enter to send, Shift+Enter for new line.
Related courses
Agent Architectures
Survey the major architectural patterns for building AI agents. From simple ReAct loops to structured planning systems, learn how different architectures trade off capability, reliability, and interpretability.
AI Fundamentals & Large Language Models
Understand the foundations of modern AI and the large language models (LLMs) that power agentic systems. Learn how neural networks learn, what makes LLMs special, and how they generate text one token at a time.
Building Production Agents
Move from prototype to production. Learn the engineering practices required to build AI agents that are reliable, observable, cost-effective, and maintainable at scale — including evaluation, tracing, error handling, and CI/CD for AI systems.