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

This paper introduces REGAL, a registry-driven architecture that ensures deterministic grounding of agentic AI in enterprise telemetry by treating telemetry computation as a first-class primitive and synthesizing version-controlled tools from declarative metric definitions to address challenges in context limits, semantic alignment, and metric evolution.

Original authors: Yuvraj Agrawal

Published 2026-03-04

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Yuvraj Agrawal

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are the CEO of a massive, chaotic city. Every day, millions of citizens (your software systems) send you raw, unfiltered reports: traffic jams, power outages, construction delays, and weather updates. You want to hire a brilliant, super-smart AI assistant (an LLM) to help you make decisions based on this data.

If you just hand the AI a live feed of every single raw report as it happens, two things will go wrong:

The AI will get overwhelmed. It's like trying to read a million newspapers at once to find one story. The AI will get confused, hallucinate (make things up), or run out of "brain space" (tokens) before it finishes.
The AI will misunderstand the jargon. One department calls a "P1" a "Critical Fire," while another calls it a "Red Alert." Without a dictionary, the AI might think they are different things.

REGAL is the solution to this problem. It's not a new type of AI; it's a new way of organizing the information before the AI ever sees it.

Here is how REGAL works, using simple analogies:

1. The "Gold Artifacts" (The Refined Report)

Instead of letting the AI read raw, messy logs, REGAL acts like a high-end newsroom editor.

The Raw Data (Bronze): This is the chaotic pile of raw emails, tweets, and sensor readings.
The Process (Silver): The editor cleans up the grammar, checks the facts, and organizes the stories.
The Gold Artifact: This is the final, polished, 1-page summary. It's short, accurate, and version-controlled (meaning if you print it today, it's exactly the same as if you printed it yesterday).

The Rule: The AI is never allowed to read the messy raw data. It can only read the "Gold Artifacts." This keeps the AI's job simple and prevents it from getting confused by noise.

2. The "Registry" (The Master Menu)

In many companies, the tools the AI uses are like a restaurant where the menu changes every day, but the kitchen keeps cooking the old dishes. The AI asks for "Spicy Tacos," but the kitchen serves "Mild Tacos" because the recipe changed. This is called Tool Drift, and it's dangerous.

REGAL fixes this with a Master Menu (The Registry).

Engineers write down exactly what every tool does in a single, official document (the Registry).
A robot then automatically builds the tools based only on that document.
The Magic: If the document says "Tool A checks server speed," the tool will check server speed. There is no guessing. The menu and the kitchen are perfectly synchronized.

3. The "Bounded Action Space" (The Safety Fence)

Without REGAL, an AI might try to do anything: "Let me write a new SQL query to check the database!" (This is risky; it might delete something).

With REGAL, the AI is like a tourist with a strict guidebook.

The AI can only pick from a pre-approved list of actions (e.g., "Check Server Speed," "List Recent Errors").
It cannot invent new actions or ask for raw data.
This makes the AI safer and more reliable because it can't wander off the path.

4. The "Push vs. Pull" (The Alarm Clock)

Pull (Asking): You ask the AI, "How is the server?" and it checks the "Gold Artifacts" and answers.
Push (Alerting): If the "Gold Artifacts" suddenly show a crash, the system automatically rings an alarm bell and tells the AI, "Hey, something broke, go look at this!"
Why it matters: Both methods use the same clean data. You don't have one system for dashboards and another for alarms; they are all speaking the same language.

Why is this a big deal?

Most people try to make AI smarter by giving it more powerful brains (better models). REGAL argues that the problem isn't the brain; it's the diet.

If you feed a genius a diet of junk food (raw, messy data), they will perform poorly. If you feed them a healthy, pre-prepared meal (deterministic, clean "Gold Artifacts"), they can do amazing work.

In short: REGAL builds a clean, organized, and safe kitchen so that the AI chef can cook perfect meals without ever having to sort through the garbage bin. It turns a chaotic enterprise into a well-oiled machine where the AI is a helpful assistant, not a confused guesser.

1. Problem Statement

Enterprise engineering organizations generate massive volumes of heterogeneous telemetry from version control, CI/CD pipelines, issue trackers, and observability platforms. While Large Language Models (LLMs) offer potential for agentic automation, directly exposing raw telemetry to these probabilistic models creates three critical failures:

Context Overload: Raw event volumes exceed model context limits, leading to prohibitive token costs and operational infeasibility.
Semantic Ambiguity: Organizational concepts (e.g., "P1 incident," "regression") are locally defined. Probabilistic retrieval without deterministic binding leads to inconsistent interpretations and hallucinations.
Interface Drift: Hand-coded tools and ad-hoc APIs often diverge from evolving data schemas and metric definitions, creating governance risks and unreliable agent behavior.

Current approaches like Retrieval-Augmented Generation (RAG) over raw logs or Text-to-SQL lack the necessary determinism, governance, and semantic stability required for enterprise-grade reliability.

2. Methodology: The REGAL Architecture

The paper proposes REGAL, a reference architecture that separates deterministic computation from probabilistic reasoning. The core philosophy is that LLMs should not process raw events but should instead consume pre-computed, version-controlled artifacts via a bounded, semantic interface.

The architecture consists of four primary layers:

A. Deterministic Ingestion & Medallion Pipeline (Write Path)

Source Layer: Ingests data from heterogeneous systems (Git, CI/CD, Observability).
Medallion ELT Pipeline:
- Bronze: Immutable raw payload archive.
- Silver: Schema-harmonized and validated records.
- Gold: Compact, semantically rich, deterministic metric artifacts intended for AI consumption.
Key Guarantees: The pipeline ensures replayability (re-running yields identical results), idempotency (retries do not corrupt state), and versioned transformations (metric definitions evolve explicitly).

B. Registry-Driven Semantic Compilation (The Core Innovation)

Single Source of Truth: A centralized Metrics Registry declares metric semantics, retrieval logic, and governance metadata (e.g., access control, caching policies).
"Interface-as-Code": Instead of manually writing API handlers, the system compiles the registry definitions into concrete Model Context Protocol (MCP) tools.
Bounded Action Space: Agents can only invoke tools derived from the registry. They cannot generate arbitrary SQL queries or synthesize new metrics at runtime. This eliminates tool drift and constrains the agent's action space to reduce hallucinations.

C. Interaction Model

Pull (Historical): Agents invoke compiled MCP tools to retrieve deterministic Gold artifacts for specific time windows.
Push (Real-time): Change streams propagate updates from Gold artifacts to trigger alerts or agent workflows, ensuring semantic consistency between reactive and proactive modes.

D. Non-Interference Property

The architecture enforces a strict invariant: $\frac{\partial D}{\partial P} = 0$ .

D (Deterministic): Ingestion, transformation, and compilation.
P (Probabilistic): LLM inference.
Constraint: Changes in the LLM (model choice, prompts) must never alter the underlying data computation or tool definitions.

3. Key Contributions

Registry-Driven MCP Compilation: A novel pattern where tool schemas, retrieval logic, and governance controls are synthesized from a declarative registry. This ensures alignment between tool specification and implementation, mitigating tool drift.
Deterministic Grounding Architecture: A practical reference design that explicitly separates data engineering (Medallion ELT) from AI reasoning, treating deterministic computation as a first-class primitive.
Semantic Closure: By constraining agents to a finite set of compiled tools, the system reduces the "hallucination surface" and simplifies policy enforcement (ACLs, caching) at the semantic boundary.
Prototype Validation: Implementation of a prototype demonstrating feasibility in latency, token efficiency, and operational governance.

4. Results & Validation

The authors implemented a prototype and conducted a case study on an incident investigation workflow ("Why did the iOS crash rate spike yesterday?"):

Operational Efficiency: The agent-assisted workflow reduced multi-step cross-system navigation into a single bounded interaction, consolidating context switching.
Latency: Under moderate load, artifact retrieval remained sub-second. End-to-end latency was dominated by model inference, not data access, validating that deterministic retrieval does not bottleneck the system.
Token Scaling: Token consumption scaled with the number of selected metrics rather than the number of raw log entries. Simulations showed a significant reduction in token usage compared to raw-log serialization.
Governance: Access controls and caching policies were successfully enforced at the tool interface level, ensuring compliance without complex post-hoc validation.

5. Significance and Future Directions

Significance:
REGAL shifts the paradigm of enterprise AI from "prompt engineering" to "system design." It argues that for agentic AI to be reliable in regulated environments, correctness and governance must be baked into the architecture (via deterministic computation and semantic compilation) rather than relying on the LLM to infer them. It offers a robust alternative to RAG and Text-to-SQL by prioritizing determinism, auditability, and reproducibility over maximal query expressiveness.

Future Directions:

Autonomous Remediation: Extending the architecture to support bounded corrective actions with formal safety policies.
Federated Compilation: Interoperating multiple registry domains (e.g., security, infrastructure) via federated MCP interfaces.
Causal Reasoning: Integrating causal models into the Gold layer for counterfactual analysis.
Formal Verification: Using static analysis to verify tool-definition alignment and idempotency in production.

In summary, REGAL provides a systematic architectural pattern for grounding agentic AI in enterprise telemetry, ensuring that AI agents operate on a stable, governed, and deterministic foundation.