Your agent works in the demo, then forgets the user's name, preferences, and history the next day. The model is not broken. Models are stateless by design, and bigger context windows do not fix it: re sending full history costs tokens every turn and still vanishes between sessions.

This talk maps the full landscape of agent memory: key value, vector, graph, and hybrid, plus selective memory, memory hygiene, and reasoning memory. 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