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Introducing Agent Memory on Databricks

Introducing Agent Memory on Databricks

From the official release

Today we're introducing agent memory on Agent Bricks, Databricks' agent developer platform. Agent memory improves agent quality while decreasing costs and latency by storing critical facts, preferences, and workflows that an agent should remember. It's available in Beta today, runs on Lakebase for high reliability and low latency, and is built on Databricks' latest memory research.

Without memory, agents risk rediscovering key facts, forgetting user preferences, and repeat mistakes it was already corrected on. In response, many developers are rolling their own memory solutions, but run into numerous design questions - for example, where should memories be stored? How should memories get synthesized? And once generated, how should they be retrieved?

This is a short excerpt. Read the full announcement on the official source.

Continue on databricks.com → (opens in a new window)
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