A few days ago I posted here about repeatedly re-explaining the same behavioral expectations to AI systems across projects/workflows. Especially once you start mixing: different tools/runtimes different repos/projects different workflows/context windows The discussion pushed us toward experimenting with a structured-file approach instead of continually relying on prompts and memory. Things like: when the system should ask before acting what deserves caution what counts as a task boundary what operations deserve extra scrutiny Current experiment looks something like this: session_intent: demand_at: first_write task_boundary: signals: - dir_change - file_type_shift - read_to_write_transition high_consequence: tools: - “Bash:.rm.-rf.*” - “Bash:.git.push.–force.” The interesting part so far is that behavior starts surviving context/surface changes better instead of resetting every time the workflow changes. Not really “AI governance” in the enterprise/compliance sense. More operational behavior portability. Still early — the shape is iterating week to week. Curious if others here are experimenting with similar ideas or thinking about this problem differently. submitted by /u/rohynal
Originally posted by u/rohynal on r/ArtificialInteligence