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Agents need better memory more than better models
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| The Deep View <newsletter@thedeepview.co> Unsubscribe | Wed, Oct 21, 1:36 PM (19 hours ago) | ||
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October 21, 2026 | Read online  Welcome back. New research finds that AI’s use in scientific contexts may be accelerating discovery. Meanwhile, Amazon researchers created an internal tracing system that improves its AI coding agent, Kiro, from its own runs. And Databricks new memory layer for agents could solve one of the most critical problems agents face: A better understanding of a user’s history. —Nat Rubio-Licht IN TODAY’S NEWSLETTER1. Agents need better memory more than better models2. Amazon tests a safer path to self-improving AI3. Can AI free up scientists to think bigger? ENTERPRISE Why persistent memory could transform AI agents  What if the path to more capable AI in the real world wasn’t building more powerful models? Databricks launched new technology on Tuesday that offers today’s existing models more extensive context and more persistent knowledge to improve AI performance. The company unveiled MemCurrent, a new memory layer for agents that can retain knowledge across sessions and create institutional memory for enterprises—and it offers some pretty impressive results.Databricks reported these findings on the performance of MemCurrent: Agent success rate in carrying out real-world tasks tripled from 22% to 68%, based on an internal benchmark; and it did things 26% faster and with 31% fewer tokens Using three public benchmarks, MemCurrent performed as well as giving the agent every past conversation, but did it with 12x fewer tokens. Databricks found that MemCurrent outperformed three open-source memory systems, OpenClaw, Mem0, and PropMem, on accuracy and efficiency. This is one of the hardest problems in the AI industry today. It’s why startups like Pathway have built entirely new architectures to fix the endemic memory problems in LLMs. The issue leads to a number of challenges for agents, especially wasting time and money relearning the same behaviors over and over again. That creates a tradeoff to those using agents—you can make them smarter or you can make them faster and cheaper. Databricks tackles the problem by giving agents two types of memory: episodic (the raw conversation history of what was learned) and derived (what can be discerned from the patterns, preferences, and workflows). Based on Databricks’ own internal benchmark of 114 enterprise tasks, the average task with no memory was completed 22.3% of the time. With only episodic memory, it was completed 35.8% of the time. With only derived memory, it was completed 66.1% of the time. With both episodic and derived memory, it was completed 68.4% of the time. “On your next project, you don’t have to explain the same thing again,” Michael Bendersky, research director of AI at Databricks, told The Deep View. “The agent will … store your preferences and understand how you work on projects. We can propagate these preferences … from project to project implicitly without you doing anything. “Databricks customers can start using MemCurrent today, but keep in mind that it’s an underlying architecture and not a product. It’s now integrated into the company’s AI data assistant, Genie, where it can immediately boost agents. Customers can also use the new Agent Bricks Memory, launching in beta, to add the MemCurrent capabilities to any of their own custom AI agents running any model or harness. It all happens within Databricks’ Lakebase database.  Beyond the immediate value for Databricks’ customers, MemCurrent is another sign that many of the most important breakthroughs in AI are coming from better systems surrounding the models and not just the advancements in the models themselves. Memory continues to emerge as one of the most problematic limitations of LLMs, the predominant architecture of today’s AI. The memory problem leads to inconsistencies, slow answers, and above all, inefficiencies that waste a lot of tokens, compute, and money. It’s like having an employee use their time to learn how to do something one day and then forget it at the end of the day and have to waste time learning the same thing over again the next day, and the next day—and so you keep hiring more and more employees to overcompensate. In this analogy, more and more employees are equivalent to the additional GPUs, compute, and data centers required to run agents. Right now, the industry’s solution is to just build more and more data centers, but it’s already very clear that’s not a sustainable or desirable path forward. What we need instead is efficiency breakthroughs like this one from Databricks.  |
