Your agent works in the demo, then forgets everything the next day. The model is not broken: models are stateless by design, and memory belongs to the harness you build around them. Bigger context windows do not fix it. Re sending full history burns tokens every turn and still vanishes between sessions. This talk maps the agent memory landscape through one question asked four ways: retrieval by key, by meaning, and by relationship. You will learn when key value, vector, graph, and hybrid memory each win, plus the capabilities layer on top: selective memory, hygiene against dirty and poisoned entries, and reasoning memory for auditable decisions. Leave knowing exactly which memory your agent needs and why.

What you'll learn: • Choose between key value, vector, graph, and hybrid memory using one test: do you know the key, only the intent, or the relationship • Design the write path and read path that move facts from conversations into storage and back into the context window • Evaluate the three ways to decide what is worth remembering: agent driven tools, a custom extractor, or a managed extraction service • Apply memory hygiene defenses, a write gate and selective forgetting, against dirty and poisoned memory • Implement decision traces as reasoning memory so you can audit and reverse decisions built on a bad source Outline: • Memory decay: why your agent forgets • How memory reaches the model • The four memory types, one question four ways • The capabilities layer • Choosing, and the honest limits