From Context to Memory: Your Agents Need a Real Memory Layer

Official Schedule Context

Most agents don't really have memory. They have a context window, a pile of temporary files, maybe

an AGENTS.md, and a retrieval step that attempts to build state from whatever the model can still

see. You've seen the flashy demos, but these systems fall apart when an agent needs to recover from

failure, revisit prior work, and observe if failures are less frequent over time. This talk explores

agent memory as a systems problem. Effective memory isn't just storing data: it's an evolving

knowledge layer with write filtering, consolidation, reflection, and forgetting. Agents need

persistence, and they also need structure. Raw logs and Markdown scratchpads aren't enough. A real

memory layer weights recency, combines retrieval techniques, and correlates episodic memories.

Serious agent memory is inherently multi-model. The best systems use full-text search, semantic

retrieval, graph relationships, and structured state to reconstruct context with far more precision

than filesystem grep alone. This is where databases become essential as the foundation for real

memory. Memory shapes how agents behave, adapt, and improve over time.

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