<![CDATA[Large language model confidence is an unreliable signal when used in isolation, particularly in agentic workflows where tool invocations may produce consequential or irreversible effects. This study develops a confidence-gated escalation framework for production multi-agent systems that integrates complementary uncertainty signals with context-aware escalation policies. The framework combines normalized log-probability, verbalized uncertainty detection, and a tool-call risk taxonomy into a weighted composite confidence score. A composable policy architecture subsequently maps confidence and risk conditions to four escalation mechanisms: human-in-the-loop review, model upgrading, tool restriction, and compliance logging. The compliance logging mechanism generates structured audit records containing decision-relevant information to support traceability and regulatory record-keeping. Framework adapters for LangChain, CrewAI, AutoGen, and Google ADK are incorporated to facilitate deployment within existing agentic pipelines without requiring substantial architectural modification. Validation across three anonymized production deployments demonstrates that multi-signal confidence scoring reduces false escalations by 23% compared with single-signal log-probability scoring while maintaining deployment-specific human review coverage. The evaluation further shows that explicit tool-risk assessment enables escalation decisions to account for the potential consequences of agent actions rather than relying solely on model-reported confidence. The results indicate that combining heterogeneous uncertainty signals with contextual risk information provides a more consistent basis for escalation decisions than empirical threshold calibration alone. The framework offers a lightweight, composable mechanism to improve operational reliability, human oversight, and auditability in production multi-agent systems while preserving flexibility across agent architectures, tool environments, and organizational compliance requirements]]>
