TRIPWIRE: Risk-Calibrated Commitment for Tool-Using Agents

Tool-using agents do more than make predictions: their actions modify external state, so errors with similar likelihood can have sharply different consequences. Existing selective-execution strategies largely rely on model uncertainty or coarse action categories, yet uncertainty reflects predictive difficulty rather than the consequence of allowing an action to take effect. We introduce TRIPWIRE, a framework for risk-calibrated commitment in tool-using agents. TRIPWIRE estimates the commitment risk of a proposed action from predicted failure likelihood and action irreversibility, with an application-dependent severity cost. It then combines this estimate with expected fallback failure risk to obtain avoidable commitment risk, the expected harm that escalation can prevent. We calibrate the resulting policy to a target level of residual irreversible harm among autonomously executed actions. Across multiple agent backbones and tool-use environments, TRIPWIRE reduces realized irreversible harm at matched escalation budgets relative to uncertainty-based routing and mutation gating. Oracle analyses identify failure recognition as the main bottleneck; agent representations also reveal failures beyond scalar confidence during autonomous tool execution.