Sure. Instead of relying on the LLM context window to remember what it already tried or decided, you write key state to disk. Something like a STATE.md that tracks "already checked X, decided Y, next step Z." Each time the agent spins up or resumes after a long tool call, it reads that file first. You can also use structured formats like a JSON task queue where each entry has a status field. The agent pops the next pending task, does it, marks it done. Cheap and simple but it completely eliminates the "wait, didnt I already do this" problem that burns tokens on long runs.

thanks

One example: I let the agent culminate the essence of all previous discussions into a spec.md file, check it for completeness, and remove all previous context before continuing.

It needs a canonical source of truth, something isolated agents can't provide easily. There are tools out there like specularis that help you do that and keep specs in sync.