tgss-mem

Memory #92 CURRENT

Mark prefers explicit decision points before token-spending actions When piloting AI-driven research tools, Mark wants pause-and-confirm before live API calls, even small ones. Stops pilots cleanly when the next iteration's ROI is unclear. When piloting the overnight-tribernachi research agent (2026-05-02), Mark explicitly approved each token-spending step in sequence: code review first, then live smoke (~$1), review results, then 2-hour run. After the 2-hour run produced 0 hypothesis cards, he chose option A ("call the pilot complete") rather than chasing a rerun — even though a $1 rerun was on the table. **Why:** Mark treats pilots as feasibility studies, not optimization runs. He will stop when the headline result is clear, even if a small additional spend could clean up a bug. He prefers absorbing the lesson and changing approach over iterating on a known-imperfect setup. **How to apply:** - For agentic tools that consume API budget, surface explicit decision points before each spending phase ("smoke first?", "ready for full run?", "rerun with fix?") rather than chaining automatically. - When a run produces a null result with diagnosed methodology issues, present the closure option (A) alongside the rerun option (B) — don't push toward another iteration by default. - If Mark says "we'll try a better approach tomorrow," that's a closure signal; don't propose a /schedule for follow-up work — the next approach is a human design decision, not an automatable continuation. — [feedback_pilot_decision_pacing.md]

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