How to build a portable memory for AI agents
Once models are cheap and interchangeable, the valuable part of an AI setup is your agent and the context it works from. That context is specific: your priorities this quarter, who your clients are and what each one cares about, and the decisions you’ve made along with the reasons behind them.
Video transcript
As AI models become cheaper and easier to swap, the real advantage is no longer the model itself. It is the context your agent understands: your priorities this quarter, your clients and what matters to each of them, the decisions you have made and why you made them.
The personal agents are multiplying fast. Muse, Instinct, Grok Bot, and another new option next week. Each may be great at something different, yet without shared context, every new agent starts with the same question: “Tell me about yourself.”
There is a better way. Keep your working memory in files you control, outside any single vendor. Then let every agent you try read from that same foundation. A new tool can be useful on day one. Switching takes an afternoon, not years of rebuilding history. Let vendors compete for your next task, not control your past ones.
There are suddenly a lot of personal agents: Muse, Instinct, Grok Bot and more every week. If you’re like me, you’re switching between all of them to learn what each is good at. Without a shared context, you explain yourself from scratch every time.
So I keep that memory in files I control, outside any one vendor, and every agent I try reads from them. A new one is useful on day one, and switching costs an afternoon, not years of history.
I’d rather vendors compete for my next task than hold my past ones.