Two people give the same agent the same task and get wildly different results. Most of the time it isn't the prompt — it's that one of them gave the agent a memory and the other is re-explaining themselves at the start of every session. A model is stateless by default: brilliant for ninety seconds, then a blank slate. Memory is the layer that turns a clever one-off into a teammate that knows your codebase, remembers the decision you made last week, and gets better the longer it works with you.

This is a field guide to building that layer on purpose. We start with why agents forget and the taxonomy that makes the rest make sense — working, episodic, semantic, procedural memory — then build it the way you actually should: plain files first, one fact per file, before any vector database. From there we get into the parts everyone underestimates: deciding what's even worth remembering, getting the right memory back out (retrieval is the hard half), and what to do when a memory goes stale and starts lying to you. We cover the scoping decisions — shared brain vs. per-agent, per-user vs. per-project — the read-before-act / write-after loop that makes memory compound, and when to graduate from files to a real store with versioning and redaction.

It's a companion to Building Your Agentic OS and Running the Fleet — where those build the system around the agent, this one goes deep on the single pillar that most changes how an agent feels to work with. By the end you'll have a memory you can stand up this afternoon, and the judgment to know what to put in it and what to leave out.

This is a living document and will be updated as the tools and patterns evolve.