Long-running AI agents fail not only when inference fails or tools are underspecified, but when independently evolving model and harness layers change the semantics of belief, capability, and goal commitments across their boundary - a failure class this paper terms Interface Volatility. This paper argues that Agent Epistemic Integrity (AEI) must be treated as a first-class architectural constraint, achievable only through joint model-harness design organized around an explicit interface contract. The central claim is that the model-harness interface contract is the precondition for joint design; its operational form is a four-level hierarchy - goal validity, action-archetype sequencing, tool-instance selection, and invocation-level failure discrimination - that specifies what the boundary must preserve and what structured outputs the model must return for the contract to hold across levels. This reframes long-running agent design away from flat action loops and toward contract-preserving control over persistent state. Evaluation and training should therefore derive from the contract itself, testing whether belief, tool, and goal commitments hold across session boundaries and independent layer upgrades.

翻译：[translated abstract in Chinese] 长时运行AI代理的失效不仅源于推理失败或工具定义不充分，更源于独立演化的模型层与工具层在其边界处改变了信念、能力与目标承诺的语义——本文将这类失效模式称为“接口波动性”。本文论证，“代理认知完整性”（AEI）必须被作为一等架构约束来对待，且唯有通过围绕显式接口契约组织的模型-工具联合设计才能实现。核心主张是：模型-工具接口契约是联合设计的前提条件；其操作形式为一个四级层次结构——目标有效性、行为原语序列化、工具实例选择与调用级失败判别——该结构规定了边界必须保全的内容，以及模型为保障契约跨层级成立而必须返回的结构化输出。这从根本上将长时运行代理的设计从平面化行动循环转向对持久状态的契约保持型控制。因此，评估与训练应派生自契约本身，检验信念、工具与目标承诺是否能在会话边界及独立层升级的条件下成立。