Queen-Bee Agents: A BeeSpec-Centered Architecture for Governed Enterprise MCP Orchestration

• URL: http://arxiv.org/abs/2606.06545v1
• Date: Thu, 04 Jun 2026 07:12:00 GMT
• Title: Queen-Bee Agents: A BeeSpec-Centered Architecture for Governed Enterprise MCP Orchestration
• Abstract summary: 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.<n>We evaluate the system on 59 enterprise-style tasks spanning governance-sensitive requests, retrieval-driven provisioning, scoped local execution, and chemistry workflow integration.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• DuMateBench: Evaluating Autonomous Agents in Complex Real-World Workflows [69.12339622053588]
  We introduce DuMateBench, a real-session benchmark reconstructed from anonymized and privacy-screened user sessions.<n>The resulting benchmark comprises 200 tasks spanning 8 broad scenarios and 17 fine-grained capability categories.<n>We execute these tasks in isolated Docker containers injected with three forms of real-world environmental complexity: Insufficient, Unstable, and Noisy.
  arXiv  Detail & Related papers  (2026-08-27T02:36:23Z)
• Formal Verification of Agentic Systems over Operational Data [59.98246281888422]
  Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world where they act on persistent operational data.<n>Existing approaches do not provide such system-level guarantees.<n>We study here the verification of agentic systems comprising a single LLM and a tool orchestration harness over relational operational data.
  arXiv  Detail & Related papers  (2026-08-04T13:01:30Z)
• AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities [55.23913617185508]
  Agent is an open-source, lightweight, and infrastructure for evaluating Large Language Models (LLMs)-based agents.<n>It organizes the evaluation process around three independent components, namely Benchmark, Harness and Environment.<n>It features a fault-tolerant runtime and comprehensive trajectory analysis tools to transparently diagnose failure modes like reward-hacking.
  arXiv  Detail & Related papers  (2026-07-15T11:11:17Z)
• A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution and Lazy Discovery [5.106901034158075]
  This paper presents a hierarchical, skill-based architecture for agentic orchestration.<n> Capabilities are organized as a rooted tree where internal nodes make routing decisions and leaf nodes execute deterministic tasks.<n>By replacing global memory with localized stack frames, the architecture prevents outputs from one execution branch from leaking into another.
  arXiv  Detail & Related papers  (2026-07-13T06:15:51Z)
• AgensFlow: A Coordination-Policy Substrate for Multi-Agent Systems [0.0]
  AgensFlow treats multi-agent coordination as an online policy-learning problem under partial observability.<n>AgensFlow is evaluated on two corpora: distributed-systems incident tasks and security-advisory tasks.
  arXiv  Detail & Related papers  (2026-05-26T08:10:52Z)
• Testing Agentic Workflows with Structural Coverage Criteria [0.21485350418225244]
  We present a structural testing approach for multi-agent workflow specifications.<n>The approach represents each workflow as a typed coordination graph.<n>It derives coverage obligations over reachable agents, allowed tool edges, restricted tool edges, and delegation edges.
  arXiv  Detail & Related papers  (2026-05-26T04:07:55Z)
• Towards Security-Auditable LLM Agents: A Unified Graph Representation [22.355591892994642]
  We propose Agent-BOM, a unified structural representation for agent security auditing.<n>We show that Agent-BOM can reconstruct stealthy attack chains, including cross-session memory poisoning and tool misuse.<n>Agent-BOM provides a unified and auditable foundation for root-cause analysis and security adjudication in complex agentic ecosystems.
  arXiv  Detail & Related papers  (2026-05-07T18:14:29Z)
• Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows [67.92316850084575]
  We introduce Claw-Eval-Live, a live benchmark for workflow agents.<n>Each release is constructed from public workflow-demand signals.<n>Claw-Eval-Live records execution traces, audit logs, service state, and post-run workspace artifacts.
  arXiv  Detail & Related papers  (2026-04-30T17:23:19Z)
• Small Model as Master Orchestrator: Learning Unified Agent-Tool Orchestration with Parallel Subtask Decomposition [61.291733522717415]
  Agent-as-Tool is a unified parallel orchestration paradigm that mitigates both agents and tools into a standardized, learnable action space.<n>ParaManager decouples planning decisions from subtask solving, enabling state-aware parallel subtask decomposition, delegation, and asynchronous execution.<n>Experiments show that ParaManager achieves strong performance across multiple benchmarks and exhibits robust generalization under unseen model pools.
  arXiv  Detail & Related papers  (2026-04-18T14:41:27Z)
• OrgAgent: Organize Your Multi-Agent System like a Company [75.47076168155817]
  We introduce OrgAgent, a company-style hierarchical multi-agent framework.<n>We find that multi-agent systems organized in a company-style hierarchy generally outperform other organizational structures.
  arXiv  Detail & Related papers  (2026-04-01T15:21:14Z)
• From Tool Orchestration to Code Execution: A Study of MCP Design Choices [20.817336331051752]
  Model Context Protocols (MCPs) provide a unified platform for agent systems to discover, select, and orchestrate tools across heterogeneous execution environments.<n>Recent MCP designs have incorporated code execution as a first-class capability, an approach called Code Execution MCP (CEMCP)<n>This work formalizes the architectural distinction between context-coupled (traditional) and context-decoupled (CEMCP) models, analyzing their fundamental scalability trade-offs.
  arXiv  Detail & Related papers  (2026-02-17T19:03:08Z)
• Alita-G: Self-Evolving Generative Agent for Agent Generation [54.49365835457433]
  We present ALITA-G, a framework that transforms a general-purpose agent into a domain expert.<n>In this framework, a generalist agent executes a curated suite of target-domain tasks.<n>It attains strong gains while reducing computation costs.
  arXiv  Detail & Related papers  (2025-10-27T17:59:14Z)
• AgentOrchestra: Orchestrating Hierarchical Multi-Agent Intelligence with the Tool-Environment-Agent(TEA) Protocol [22.406849007798858]
  We propose the Tool-Environment-Agent Protocol to integrate environments, agents, and tools into an unified system.<n>We introduce AgentOrchestra, a hierarchical multi-agent framework with a central planning agent that decomposes complex objectives and coordinates specialized agents.
  arXiv  Detail & Related papers  (2025-06-14T13:45:37Z)
• SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
  SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
  arXiv  Detail & Related papers  (2025-03-11T17:53:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.