Control Plane as a Tool: A Scalable Design Pattern for Agentic AI Systems
Sivasathivel Kandasamy
TL;DR
The paper tackles the bottleneck of tool orchestration in agentic AI systems that leverage LLMs. It proposes the Control Plane as a Tool pattern to decouple tool routing from agent reasoning and expose a single interface for tools. The architecture includes modules such as Registration, Invocation, Intent Resolution, Routing, Output Validation, and Feedback, enabling modular, auditable, and dynamic tool usage. Compared with Anthropic's Model Context Protocol, the Control Plane supports multi-agent coordination, governance, and extensibility, and the paper outlines future work to evaluate performance and safety in larger deployments.
Abstract
Agentic AI systems represent a new frontier in artificial intelligence, where agents often based on large language models(LLMs) interact with tools, environments, and other agents to accomplish tasks with a degree of autonomy. These systems show promise across a range of domains, but their architectural underpinnings remain immature. This paper conducts a comprehensive review of the types of agents, their modes of interaction with the environment, and the infrastructural and architectural challenges that emerge. We identify a gap in how these systems manage tool orchestration at scale and propose a reusable design abstraction: the "Control Plane as a Tool" pattern. This pattern allows developers to expose a single tool interface to an agent while encapsulating modular tool routing logic behind it. We position this pattern within the broader context of agent design and argue that it addresses several key challenges in scaling, safety, and extensibility.