Blog

AI agent memory, explained

Deep dives on context engineering, retrieval, and the memory infrastructure behind reliable AI agents, written by the team building stored.

Multi-Agent & Teams

Multi-Tenant AI Memory: Team Roles and Access Control

Sharing memory within a team is good. Leaking it across teams is a breach. Here's how multi-tenant AI memory keeps the two apart.

RAG

Chunking Strategies RAG: A Practical Guide

Bad chunking quietly ruins good retrieval. Here's how to actually split documents for RAG and agent memory.

RAG

Context Compression: Fitting More Into a Finite Window

A January 2026 paper cut token usage 22.7% with no accuracy loss. Here's how context compression actually works.

Trust & Governance

AI Memory Provenance: Knowing Where a Memory Came From

An agent that's wrong is a bug. An agent that's wrong and unauditable is a liability. Provenance is the difference.

Graph & Persistent Memory

Persistent Memory LLM: Beyond the Chat Session

An LLM has no memory of its own. Persistent memory is the external layer that makes it behave like it does.

Multi-Agent & Teams

Shared Memory AI Agents Use: One Context, Every Tool

Your coding agent and your support agent shouldn't learn everything twice. Here's how shared memory for AI agents actually works.

Fundamentals

Context Window: What It Is and Why It Isn't Enough

A million-token context window sounds like enough. Here's why it still isn't a substitute for real AI agent memory.

Fundamentals

Short-Term vs Long-Term Memory in AI Agents

An agent needs a scratchpad and a filing cabinet. Here's the real difference between short-term and long-term memory, and why both matter.

Graph & Persistent Memory

Long-Term Memory AI Agents: How It Actually Works

A chat history isn't long-term memory. Here's what actually makes AI agent memory durable across sessions, tools, and time.

Vectors & Search

Semantic Search: How It Works and Where It Falls Short

Semantic search finds what you meant, not just what you typed. Here's how it works and when it still isn't enough.

Vectors & Search

Hybrid Search: Combining Vector, Keyword, and Graph Retrieval

No single retrieval method catches everything. Hybrid search combines vector, keyword, and graph lookup into one ranked result.

Graph & Persistent Memory

Knowledge Graph for AI Agents: A Practical Guide

Vector search finds similar text. It doesn't answer relationship questions. A knowledge graph for AI agents does.

Vectors & Search

Vector Embeddings for AI Agents, Explained

Every semantic search and memory system depends on turning text into numbers. Here's what vector embeddings actually are.

Vectors & Search

Vector Database: What It Is and How to Choose One

Every RAG and agent-memory pipeline needs somewhere to store embeddings. Here's what a vector database actually does and how to pick one.

RAG

RAG vs Long Context: Which Should You Use?

Context windows now run past a million tokens. That doesn't make RAG obsolete. Here's how to actually choose between them.

RAG

Retrieval-Augmented Generation (RAG): A Practical Guide

RAG is the default way to ground an LLM in facts it wasn't trained on. Here's how it actually works and where it breaks down.

MCP

MCP Server: What It Is and How to Choose One

An MCP server is what turns a protocol into something you can actually connect to. Here's what to look for before you pick or build one.

MCP

Model Context Protocol (MCP) Explained

MCP is the plumbing that lets Claude, Cursor, and Claude Code all talk to the same tools and memory. Here's how it actually works.

Fundamentals

Context Engineering: The Discipline Behind Reliable Agents

Most agent failures aren't model failures. They're context failures. Here's what context engineering is and how to get it right.

Fundamentals

AI Agent Memory: What It Is and How to Build It

Context windows reset. AI agent memory doesn't. Here's what agent memory actually is, why it matters, and how teams build it in production.