Replace or Repair? Isolating Handover Value in Stalled Agents

When a long-horizon agent stalls, should its manager replace it with a successor built for the current state, or do advice and a clean context suffice? Existing trajectories cannot answer this: they confound replacement's benefit with that of the advice and context supplied alongside it. We isolate the effect with a 2×2 factorial that resumes every arm from the same frozen snapshot of a real stalled run under a fixed budget and executor, crossing context (keep or rebuild) against advice (withhold or supply). Revise and Replace share one generator's suggestion verbatim; Fresh and Replace share one handover package; all arms reread the same logs at equal charge. The interaction term measures what rebuilding the executor buys beyond advice and context repair alone — a diagnostic, not a claim that identity confers ability; if either subsumes the gain, replacement's contribution reduces to context repair. On these causal estimates we train a budget-aware manager that selects among interventions by expected value rather than the incumbent's failure risk, and evaluate it against failure-risk triggers, periodic restarts, prompted management, and learned context editing in Craftax, the Factorio Learning Environment, SWE-bench Pro, and ARC-AGI-3. The frozen manager is then evaluated on unseen predecessor protocols, a second model family, and shifted budgets.