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

arXiv – CS AI|Yuvraj Agrawal|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers present REGAL, a registry-driven architecture that enables AI agents to work deterministically with enterprise telemetry data from systems like CI/CD pipelines and observability platforms. The system addresses key challenges of grounding Large Language Models on private enterprise data through structured data processing and version-controlled action spaces.

Key Takeaways

→REGAL addresses three key challenges in enterprise AI: limited model context, locally defined semantic concepts, and evolving metric interfaces.
→The architecture uses a Medallion ELT pipeline to create replayable, semantically compressed data artifacts for AI consumption.
→A registry-driven compilation layer automatically generates tools from declarative metric definitions, ensuring consistency between specification and execution.
→The system treats deterministic telemetry computation as a first-class primitive rather than having LLMs operate on raw event streams.
→A prototype implementation demonstrates improved latency, token efficiency, and operational governance for enterprise AI systems.

#enterprise-ai #llm-grounding #telemetry #ai-architecture #deterministic-systems #enterprise-automation #data-pipeline #ai-governance

Read Original →via arXiv – CS AI

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