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Introducing MemCurrent, a persistent memory architecture designed to help production agents stop starting from zero. MemCurrent combines episodic memory with derived memory across three access patterns: • Plugins to log and load context automatically • Tools the agent calls on demand • An offline “dreaming” pipeline that consolidates memory... in the background Agents carry forward user preferences, prior corrections, metric definitions and successful workflows instead of rediscovering them every session. On our internal benchmark built from real enterprise data, MemCurrent triples the agent's pass rate while using 31% fewer tokens and running 26% faster. It's built into Agent Bricks' Memory offering and already powers memory in Genie. Read the research:show more

Databricks

98,966 subscribers

41,579 次观看 • 5 天前 •via X (Twitter)

健康养生教育科学技术

Anya Rossi• Live Now

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13 条评论

Dai Macedo 的头像

Dai Macedo4 天前

Can you trace a derived memory back to the original runs? When an agent learns the wrong lesson, I'd want to see where it came from.

Memoket 的头像

Memoket4 天前

Two identical requests could produce different results later, even with the same model and prompt, because the agent's memory has changed. That makes memory state another thing developers need to track when debugging unexpected behavior.

Kala 的头像

Kala4 天前

please let it dream about something nicer than metric definitions

Yician Wang 的头像

Yician Wang4 天前

@bot 和我的 Agent Memory 有什麼差別？

Maya 的头像

Maya5 天前

The underrated part is remembering why a correction happened. "Use this metric" is useful. "Use it because the old one double-counted refunds" lets the next agent avoid the same mistake with a different formula.

CoinWager 的头像

CoinWager4 天前

@databricks multi-profile persistent memory is the unlock. most agents reset every session which makes them useless for real work

Harrison 的头像

Harrison4 天前

did the episodic-only ablation get a session index at startup too? curious how much of the derived-memory gain is better distillation versus making the history discoverable.

Aapakari 的头像

Aapakari5 天前

Tripling pass rate sounds big until you see the 22.3% no-memory floor.

Jack 的头像

Jack4 天前

an offline dreaming pipeline, cute name for homework

Cmorris 的头像

Cmorris4 天前

Honest question: when the background cleanup replaces a fact, can you see what it used to say?

Alessandro Moretti 的头像

Alessandro Moretti5 天前

So the agents dream at night now. They sleep better than most founders.

Driven Dynamics 的头像

Driven Dynamics5 天前

Carrying forward corrections and business context is a big step for enterprise agents. One interesting challenge is separating what an agent learned previously from what's operationally true right now. A rule, approval, or exception might have changed in the underlying system. Persistent memory becomes even more valuable when it knows what needs to be revalidated before acting.

MASA 的头像

MASA5 天前

How does the dreaming pipeline handle outdated or conflicting corrections?

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132,225 次观看 • 1 年前

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LLMs are stateless. We built Dria Mem Agent to change that: Making memory a first-class feature. A 4B agent with local interoperable memory across Claude, ChatGPT and LM Studio. It turns LLMs from stateless chat into stateful agents with persistent human-readable memory.

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201,127 次观看 • 1 年前

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13,681 次观看 • 4 个月前

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13,015 次观看 • 2 个月前

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22,915 次观看 • 7 个月前

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38,794 次观看 • 1 个月前

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20,567 次观看 • 6 个月前

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13,082 次观看 • 9 个月前

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36,894 次观看 • 25 天前

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47,190 次观看 • 19 天前

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28,980 次观看 • 6 个月前

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83,511 次观看 • 7 个月前