A lot of 'agentic harnesses' actually do have limited memory functions these days. In the simplest form, the LLM can write to a file like memory.md or claude.md or agent.md , and this gets tacked on to their system prompt going forwards. This does help a bit at least.

Rather more sophisticated Retrieval Augmented Generation (RAG) systems exist.

At the moment it's very mixed bag, with some frameworks and harnesses giving very minimal memory, while others use hybrid vector/full text lookups, diverse data structures and more. It's like the cambrian explosion atm.

Thing is, this is probabilistic, and the influence of these memories weakens as your context length grows. If you don't manage context properly, (and sometimes even when you think you do), the LLM can blow past in-context restraints, since they are not 100% binding. That's why you still need mechanical safeguards (eg. scoped credentials, isolated environments) underneath.