Queen-Bee Agents achieve 96.4% task success with zero governance failures in enterprise MCP orchestration

Queen-Bee introduces a governed multi-agent architecture designed for enterprise environments where LLMs must connect to private tools, internal knowledge, and Model Context Protocol (MCP) interfaces. The system features a Queen control plane that retrieves capabilities, plans task-scoped execution, and compiles a structured BeeSpec. Specialized Bee agents then execute these plans under constrained tool access, ensuring policy enforcement, tenant-scoped isolation, and operational boundaries. The prototype includes tenant-scoped MCP connectors, audit-backed execution-time governance, retrieval-driven weak incubation, and multiple provisioning backends.

Evaluated 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 achieved 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. The researchers also demonstrated a multi-Bee chemistry workflow with explicit approval gating and a concrete top-3 shortlist grounded in real upstream evidence. While hybrid retrieval and LLM-guided provisioning backends are viable, they did not outperform the lightweight structured retriever on the current small, highly structured capability registry. The results provide prototype-level systems evidence and suggest 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.