If your agent's memory lives inside one model provider, switching models or harnesses means starting over. You need portable memory - yet when switching agent memory between models, you can swing accuracy by thirteen points or more, depending on which model reads them back.

Kimberly Logan, Head of Product at the Walrus Foundation, where she builds Walrus Memory, joins Chain of Thought to discuss how to build portable and verifiable memory for AI agents - and why Walrus's decentralized storage network has proved an apt primitive. She and host Conor Bronsdon get into why model lock-in is a memory problem, what people use agent memory for beyond coding, how you prove what happened to your data, and why Walrus doesn't publish memory quality benchmarks, plus more. This episode is sponsored by Walrus.

We cover:

Why longer context windows don't help once you switch models or harnesses, and why an import function only fixes one point in time
Why Kimberly doesn't benchmark Walrus Memory against memory startups, and why she sees the opportunity as bigger than memory alone
What people build with Walrus Memory besides coding agents, which Walrus puts at maybe 30% of usage: long-running study aids, trading agents, and security teams comparing production snapshots with code changes
Walrus's figure that almost 85% of the memories written are read back more than 30 days later
How attestations on the blockchain and a delegate key you control let you show what happened to your data, and why unchanged data still isn't proof that it's accurate
Why Kimberly treats recall quality and latency as table stakes, and how a knowledge graph keeps recall on the freshest memories
Why she left almost 20 years in traditional software, including seven at Google, for a blockchain company, and where shared memory across companies and trust boundaries goes next
Walrus Console, announced this week: one place to manage agent memories and files through an interface or an MCP connection, without knowing how the storage underneath is configured

Connect with Kimberly Logan:

LinkedIn: https://www.linkedin.com/in/krlgn/
Twitter/X: https://x.com/kimblgn
Walrus: https://walrus.xyz/cot

Connect with Chain of Thought host Conor Bronsdon:

🔗 More episodes: https://chainofthought.show

Chapters:
(0:00) Why agent memory has to move with you
(1:25) Context windows don't follow you to a new model
(4:41) Why an import function doesn't fix lock-in
(6:38) Why Walrus isn't competing head-on with memory startups
(8:39) Where Walrus Memory goes next
(10:38) Trading agents and security snapshots on Walrus Memory
(14:44) Verifiability: answering what happened to your data
(17:21) Fresh recall, and why Walrus doesn't publish memory benchmarks
(20:22) From traditional software to a blockchain company
(24:40) A record other companies and regulators can check
(25:42) What Walrus Console is for
(29:09) Use agent memory beyond coding agents
(30:52) Plugging in your own agent stack
(31:41) Memory across companies and trust boundaries
(35:48) Closing thoughts

Thanks to Walrus, presenting sponsor of season four of Chain of Thought. Walrus Memory gives AI agents portable, verifiable memory that carries context across apps, sessions, and other agents. Get started at https://walrus.xyz/cot

#AI #AIAgents #AgentMemory #SoftwareEngineering #DevTools

Switching Models Shouldn't Mean Starting Over | Walrus's Kimberly Logan

Chain of Thought | AI Agents, Infrastructure & Engineering

Switching Models Shouldn't Mean Starting Over | Walrus's Kimberly Logan