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REGAL: A Registry-Driven Architecture for Deterministic Grounding of Agentic AI in Enterprise Telemetry

Yuvraj Agrawal

TL;DR

REGAL is presented, a registry-driven architecture for deterministic grounding of agentic AI systems in enterprise telemetry that elevates deterministic computation and semantic compilation to first-class design primitives for agentic systems.

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.

REGAL: A Registry-Driven Architecture for Deterministic Grounding of Agentic AI in Enterprise Telemetry

TL;DR

REGAL is presented, a registry-driven architecture for deterministic grounding of agentic AI systems in enterprise telemetry that elevates deterministic computation and semantic compilation to first-class design primitives for agentic systems.

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.
Paper Structure (56 sections, 3 equations, 3 figures, 1 table)

This paper contains 56 sections, 3 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem summary: limitations of probabilistic grounding
  3. Key idea: registry-driven semantic compilation
  4. Scope and contributions
  5. Paper roadmap
  6. The Reference Architecture
  7. Architectural overview
  8. Deterministic–probabilistic non-interference (concise property)
  9. Hybrid push–pull interaction model
  10. Design principles and invariants (operational form)
  11. Model-agnostic data invariant.
  12. Idempotent ingestion and replayability.
  13. Context compression as semantic engineering.
  14. Semantic consistency via single-source registry.
  15. Operational consequences
  16. ...and 41 more sections

Figures (3)

  • Figure 1: High-level architecture: sources are deterministically refined into versioned Gold artifacts; agentic LLMs consume only those artifacts. The thick boundary emphasizes the deterministic $\rightarrow$ probabilistic handoff.
  • Figure 2: Read path and serving: the registry compiles declarative metric definitions into MCP tools; agents invoke MCP tools (pull) and the Gold layer can also drive alerts (push). Governance (ACLs, audit) is enforced at the MCP boundary.
  • Figure 3: Write path (Medallion ELT): ingestion, deterministic upserts, Bronze (raw archive), Silver (schema harmonization), and Gold (AI context artifacts).