Recoleta Item Note
REGAL: A Registry-Driven Architecture for Deterministic Grounding of Agentic AI in Enterprise Telemetry
REGAL proposes an agentic AI deployment architecture for enterprise telemetry: first, a deterministic data pipeline compresses raw telemetry into reproducible semantic metrics, and then the LLM accesses these results…
agentic-aienterprise-telemetrymcp-toolsdeterministic-groundingregistry-driven-architecture
Summary
REGAL proposes an agentic AI deployment architecture for enterprise telemetry: first, a deterministic data pipeline compresses raw telemetry into reproducible semantic metrics, and then the LLM accesses these results only through controlled tools compiled from a registry. Its core value lies not in new learning algorithms, but in unifying “semantic definitions, tool interfaces, and governance policies” into versionable architectural constraints.
Problem
- Enterprise engineering telemetry comes from version control, CI/CD, defect tracking, and observability platforms; the data is large-scale, heterogeneous, and continuously evolving, so feeding raw events directly to an LLM causes context overload and high token costs.
- Internal organizational semantics (such as “P1,” “regression,” and “release candidate”) are locally defined; if one relies only on probabilistic retrieval or RAG, the model can easily misinterpret semantics and hallucinate.
- Handwritten tools/APIs can experience tool drift as schemas and metric definitions change, causing interface documentation, actual execution, and governance policies to fall out of sync, which affects auditing and compliance.
Approach
- It adopts a deterministic–probabilistic separation: all telemetry first goes through a replayable, idempotent, versioned Medallion ELT process, transforming Bronze/Silver into Gold semantic artifacts for AI consumption; the LLM can only consume these artifacts and cannot in turn alter the computation logic.
- It introduces a metrics registry as the single source of truth: the registry declares metric identifiers, semantic descriptions, retrieval logic, platform scope, ACL, and caching policies.
- Through registry-driven compilation, declarative metric definitions are automatically compiled into MCP tools, including tool schema, descriptions, access control, and caching behavior, thereby realizing “interface-as-code” at runtime.
- Through a bounded action space, the agent is restricted to calling a limited set of precompiled semantic tools, rather than generating arbitrary SQL or accessing raw logs, reducing the hallucination surface and governance complexity.
- It supports dual pull + push paths: historical analysis pulls Gold metrics by time window through tools; real-time monitoring triggers alerts and agent workflows through Gold-layer change streams, with both sharing the same semantic boundary.
Results
- The paper explicitly states that this is a systems architecture and prototype validation effort; it does not report precise quantitative comparison results on standard benchmark datasets, and it does not propose new learning algorithms.
- The prototype and case study claim to validate the architecture’s value in feasibility, latency, token efficiency, and governance; the most specific numerical statement in the paper is that when Gold artifacts fit in memory and under moderate concurrent load, aggregated metric retrieval can remain sub-second, and responses are almost instantaneous when the cache is hit.
- The paper further claims that in the prototype scenario above, model inference latency rather than data retrieval latency becomes the dominant part of end-to-end interaction, supporting its design principle of “deterministic computation first, probabilistic reasoning second.”
- The main breakthrough claim is not about SOTA metrics, but about architecture: using the registry as the single source of truth, using the compilation step as the mechanism for tool consistency and governance enforcement, and treating deterministic Gold artifacts as the LLM’s sole input boundary, in order to mitigate the three enterprise deployment pain points of context overload, local semantics, and tool drift.
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