A single agent task can turn into dozens of steps, and the data behind them piles up fast. Keeping it all in RAM keeps agents fast, but it gets expensive. Join us as we look at three areas where this problem shows up, and how to design for them as your agentic architecture scales.
What we'll cover:
Context: KV caching to avoid recomputing shared context across agent steps.
Retrieval: Search and long-term memory over terabyte-scale data.
Decisions: Feature stores that give agents fresh data for decisions like fraud detection and personalization.
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