Agents
Relying on model providers' stateful APIs or harnesses creates lock-in: switching models means losing your agent's memory -- a cost that onl…
Relying on model providers' stateful APIs or harnesses creates lock-in: switching models means losing your agent's memory -- a cost that only grows as agents get better at learning a big part of agent
Relying on model providers' stateful APIs or harnesses creates lock-in: switching models means losing your agent's memory -- a cost that only grows as agents get better at learning a big part of agent harnesses is how they interact with context memory is just context its therefor impossible to separate harness from memory - as @sarahwooders says, "memory isn't a plugin (it's a harness)"
Related
- “Memory is important, and it creates lock in” Exactly why I don’t want OpenAI, Anthropic or any of the other AI companies owning it.
- > which is exactly why we believe memory should live outside of model providers open harness = open memory which everyone should want!
- 'Harnesses are intimately tied to memory, which means that by choosing an open harness you are choosing to own your memory, and not have it …
- Great piece. The lock-in point is the one nobody talks about enough. If your agent’s memory lives behind someone else’s API, you don’t have …
- Great breakdown of how model providers are platformizing their AI/agents. A lot of people will take the convenience of going all in on a pro…
- This 10-min read from @Vtrivedy10 changes how you build AI agents. Most people are stuck in the same loop; switching models when agents brea…
Source: Harrison Chase (X) | 2026-04-11