用可审计的指标评估AI代理真实上线能力，避免盲目部署。

AI agents are moving into production workflows where they retrieve information, call tools, maintain state, and act on behalf of users or organizations, but many release decisions still rely on capability signals, demos, or behavioral tests that do not show whether an agent is ready to operate under production constraints. Capability is therefore not production readiness. This paper introduces the ProofAgent Index (PAI), a governance readiness index for AI agents. PAI combines four dimensions of deployment evidence: Evaluation, Context, Compliance, and Governance. Evaluation measures observed behavior, Context measures the operating environment that shapes that behavior, Compliance measures alignment with applicable rules and controls, and Governance measures whether the organization can authorize, monitor, audit, and control the agent during operation. PAI is implemented inside ProofAgent Harness, an open source infrastructure for auditable AI agent evaluation and governance. Validation across two heavily regulated domains, healthcare and finance, shows that PAI carries held out readiness signal and separates higher risk from lower risk configurations. The results show that context engineering strongly changes reliability, capability improves behavior but does not determine readiness, and governance evidence must remain visible rather than averaged away. PAI reframes agent release from a faith based deployment decision into an auditable readiness decision.