蜂后智能体：一种以BeeSpec为中心的受治理企业MCP编排架构
Queen-Bee Agents: A BeeSpec-Centered Architecture for Governed Enterprise MCP Orchestration
AI总结：
提出Queen-Bee多智能体架构，通过Queen控制平面检索能力、规划任务并编译BeeSpec，由Bee智能体在受限工具访问下执行，实现策略执行、租户隔离和边界内执行，在59个企业任务中达到0.964成功率且零治理失败。
摘要：
企业智能体系统日益需要将大语言模型连接到私有工具、内部知识和模型上下文协议（MCP）接口。在这种环境下，原始任务能力是不够的：组织还需要策略执行、租户范围的隔离以及在明确操作边界内的执行。我们提出了Queen-Bee，一种受治理的多智能体架构，其中Queen控制平面检索能力、规划任务范围的执行，并编译结构化的BeeSpec，由专门的Bee智能体在受限工具访问下执行。我们实现了一个工作原型，具有租户范围的MCP连接器、审计支持的运行时治理、检索驱动的弱孵化和多个配置后端。我们在59个企业风格任务上评估了系统，涵盖治理敏感请求、检索驱动的配置、范围化本地执行和化学工作流集成。检索驱动的Queen-Bee变体实现了0.964的任务成功率、零治理失败，并且在范围化执行质量上显著优于静态Queen-Bee基线和宽松的单智能体基线。我们进一步展示了一个多Bee化学工作流，具有明确的审批门控和一个基于真实上游证据和筛选工件的具体前三候选名单。与混合检索和LLM引导配置的额外比较表明，更丰富的配置后端是可行的，但在当前小型、高度结构化的能力注册表上并未优于轻量级结构化检索器。结果提供了原型级别的系统证据，而非生产部署研究，并表明企业智能体平台不仅应根据能力进行评估，还应考虑受治理的配置、隔离行为、范围化执行质量和工件感知的工作流协调。
英文摘要：
Enterprise agent systems increasingly need to connect large language models to private tools, internal knowledge, and Model Context Protocol (MCP) interfaces. In this setting, raw task capability is insufficient: organizations also require policy enforcement, tenant-scoped isolation, and execution that remains within explicit operational boundaries. We present Queen-Bee, a governed multi-agent architecture in which a Queen control plane retrieves capabilities, plans task-scoped execution, and compiles a structured BeeSpec that is executed by specialized Bee agents under constrained tool access. We implement a working prototype with tenant-scoped MCP connectors, audit-backed execution-time governance, retrieval-driven weak incubation, and multiple provisioning backends. We evaluate the system on 59 enterprise-style tasks spanning governance-sensitive requests, retrieval-driven provisioning, scoped local execution, and chemistry workflow integration. The retrieval-driven Queen-Bee variant achieves a task success rate of 0.964, zero governance failures, and substantially better scoped execution quality than both a static Queen-Bee baseline and a permissive single-agent baseline. We further show a multi-Bee chemistry workflow with explicit approval gating and a concrete top-3 shortlist grounded in real upstream evidence and screening artifacts. Additional comparisons with hybrid retrieval and LLM-guided provisioning show that richer provisioning backends are viable but do not outperform the lightweight structured retriever on the current small, highly structured capability registry. The results provide prototype-level systems evidence rather than a production deployment study, and suggest that enterprise agent platforms should be evaluated not only by capability, but also by governed provisioning, isolation behavior, scoped execution quality, and artifact-aware workflow coordination.
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