Enterprise software engineering is shifting away from deterministic CRUD/REST architectures toward AI-native systems where large language models act as cognitive orchestrators. This transition introduces a critical security tension: probabilistic LLMs weaken classical mechanisms for validation, access control, and formal testing. This paper proposes the design, formal validation, and empirical evaluation of a Semantic Gateway governed by the Model Context Protocol (MCP). The gateway reframes the enterprise API as a semantic surface where tools are dynamically discovered, authorized, and executed based on intent and policy enforcement. The central contribution rests on a paradigm shift: autonomous agents must not be validated as traditional software nor as simple API consumers, but as stochastic state-transition systems whose behavior must be abstracted, fuzzed, and audited through enabled-tool graphs. The architecture introduces a three-layer Zero-Trust security model comprising a pre-inference Semantic Firewall, deterministic Tool-Level RBAC, and out-of-band Cryptographic Human-in-the-Loop approval. Enabledness-Preserving Abstractions (EPAs) and greybox semantic fuzzing--originally developed for blockchain smart contract verification--are adapted to audit agent behavior in enterprise environments. Results demonstrate an 84.2% reduction in incidental code. Across 500,000 multi-turn fuzzing sequences, the methodology achieved a 100% discovery rate of hidden unauthorized state transitions, proving that dynamic formal verification is strictly necessary for secure agentic deployment.

翻译：企业软件工程正从确定性的CRUD/REST架构向AI原生系统转型，其中大语言模型作为认知编排器发挥作用。这一转变引入了关键的安全张力：概率性LLM削弱了传统验证机制、访问控制与形式化测试的有效性。本文提出一种基于模型上下文协议（MCP）的语义网关设计、形式化验证与实证评估。该网关将企业API重塑为语义接口，工具在其中基于意图与策略执行被动态发现、授权和调用。核心贡献在于范式转变：自主智能体不能作为传统软件或简单API消费者进行验证，而应视为随机状态转移系统，其行为必须通过启用工具图进行抽象、模糊测试和审计。该架构引入三层零信任安全模型，包含推理前语义防火墙、确定性工具级RBAC以及带外加密人工审批机制。将原本用于区块链智能合约验证的启用保持抽象（EPA）与灰盒语义模糊测试技术适配至企业环境中智能体行为审计。实验结果表明，代码偶然性降低84.2%。在50万次多轮模糊测试序列中，该方法实现了隐藏未授权状态转移的100%发现率，证明动态形式化验证对安全部署智能体具有绝对必要性。