arXiv:2606.06545v1 Announce Type: cross 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 sp
The proliferation of large language models and their integration into enterprise systems necessitates robust architectural solutions for governance, security, and controlled deployment within organizational boundaries.
This development addresses critical challenges in deploying AI agents responsibly within enterprises, enhancing trust and enabling broader adoption by ensuring policy enforcement and secure operation.
Enterprises can now implement multi-agent AI systems with explicit operational boundaries, policy enforcement, and tenant-scoped isolation, moving beyond raw task capability to governed AI deployments.
- · Enterprise AI software providers
- · Organizations adopting AI agents
- · Cybersecurity firms specializing in AI governance
- · Undeveloped, unsecured AI solutions
- · Organizations slow to adopt governed AI architectures
Enterprises gain more control and confidence in deploying sophisticated AI agent systems for critical tasks.
This framework could lead to a rapid expansion of AI agent applications across sensitive corporate functions.
The enhanced security and governance may set new industry standards for responsible AI deployment and accelerate the 'collapse' of white-collar workflows.
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Read at arXiv cs.AI