REGAL: A Registry-Driven Architecture for Deterministic Grounding of Agentic AI in Enterprise Telemetry
arXiv:2603.03018v1 Announce Type: new Abstract: Enterprise engineering organizations produce high-volume, heterogeneous telemetry from version control systems, CI/CD pipelines, issue trackers, and observability platforms. Large Language Models (LLMs) enable new forms of agentic automation, but grounding such agents on private telemetry raises three practical challenges: limited model context, locally defined semantic concepts, and evolving metric interfaces. We present REGAL, a registry-driven architecture for deterministic grounding of agentic AI systems in enterprise telemetry. REGAL adopts an explicitly architectural approach: deterministic telemetry computation is treated as a first-class primitive, and LLMs operate over a bounded, version-controlled action space rather than raw event streams. The architecture combines (1) a Medallion ELT pipeline that produces replayable, semantically compressed Gold artifacts, and (2) a registry-driven compilation layer that synthesizes Mode
arXiv:2603.03018v1 Announce Type: new Abstract: Enterprise engineering organizations produce high-volume, heterogeneous telemetry from version control systems, CI/CD pipelines, issue trackers, and observability platforms. Large Language Models (LLMs) enable new forms of agentic automation, but grounding such agents on private telemetry raises three practical challenges: limited model context, locally defined semantic concepts, and evolving metric interfaces. We present REGAL, a registry-driven architecture for deterministic grounding of agentic AI systems in enterprise telemetry. REGAL adopts an explicitly architectural approach: deterministic telemetry computation is treated as a first-class primitive, and LLMs operate over a bounded, version-controlled action space rather than raw event streams. The architecture combines (1) a Medallion ELT pipeline that produces replayable, semantically compressed Gold artifacts, and (2) a registry-driven compilation layer that synthesizes Model Context Protocol (MCP) tools from declarative metric definitions. The registry functions as an "interface-as-code" layer, ensuring alignment between tool specification and execution, mitigating tool drift, and embedding governance policies directly at the semantic boundary. A prototype implementation and case study validate the feasibility of deterministic grounding and illustrate its implications for latency, token efficiency, and operational governance. This work systematizes an architectural pattern for enterprise LLM grounding; it does not propose new learning algorithms, but rather elevates deterministic computation and semantic compilation to first-class design primitives for agentic systems.
Executive Summary
REGAL, a registry-driven architecture, addresses the practical challenges of grounding agentic AI systems in enterprise telemetry by adopting a deterministic approach. The architecture combines a Medallion ELT pipeline and a registry-driven compilation layer to produce semantically compressed Gold artifacts and synthesize Model Context Protocol tools from declarative metric definitions. A prototype implementation and case study validate the feasibility of deterministic grounding, demonstrating its implications for latency, token efficiency, and operational governance. This work systematizes an architectural pattern for enterprise Large Language Model grounding, elevating deterministic computation and semantic compilation to first-class design primitives.
Key Points
- ▸ REGAL adopts a deterministic approach to grounding agentic AI systems in enterprise telemetry
- ▸ The architecture combines a Medallion ELT pipeline and a registry-driven compilation layer
- ▸ Deterministic grounding demonstrates implications for latency, token efficiency, and operational governance
Merits
Strength
REGAL addresses the practical challenges of grounding agentic AI systems in enterprise telemetry, providing a systematic approach to deterministic grounding.
Demerits
Limitation
REGAL's reliance on a registry-driven architecture may introduce complexity and overhead, potentially limiting its adoption in certain enterprise environments.
Expert Commentary
REGAL represents a significant contribution to the field of agentic AI and enterprise telemetry, providing a systematic approach to deterministic grounding. While the architecture's reliance on a registry-driven approach may introduce complexity, its implications for latency, token efficiency, and operational governance are compelling. As the use of Large Language Models continues to grow in enterprise environments, REGAL's focus on deterministic grounding and semantic compilation will be increasingly relevant. Furthermore, REGAL's approach to governance and policy may inform the development of AI governance policies and regulations, highlighting the need for more systematic and transparent approaches to AI decision-making.
Recommendations
- ✓ Future research should investigate the scalability and adaptability of REGAL in larger enterprise environments
- ✓ The development of standardized governance policies and regulations for agentic AI systems should be informed by REGAL's approach to semantic compilation and governance