Graph, Loop, and Harness Engineering for Zero-Trust Agentic Data Engineering and Analytical Processing

Abstract:Large language model agents increasingly automate data workflows, but end-to-end cloud data engineering and analytical execution require reliable coordination across code, data, infrastructure, and runtime environments. We present two zero-trust frameworks. Zero-Trust Agentic Data Engineering generates, deploys, and verifies complete cloud data-engineering solutions from natural-language tasks, with completion conditioned on repository, deployment, runtime, and policy evidence. Zero-Trust Agentic OLAP combines governed Data Preparation with verified Online Analytical Processing (OLAP), permitting production promotion only after validation and evidence-bound approval, and releasing analytical answers only after Same-Snapshot Execution, Exact Result Equivalence, deterministic grounding, and reflection. Both frameworks share three abstractions: graph engineering for evidence-gated workflow structure, loop engineering for bounded recovery, and agent-harness engineering for zero-trust execution. We evaluate both frameworks under nominal execution, controlled failures, bounded recovery, and policy-constrained conditions, measuring verified completion, recovery, authorization enforcement, production promotion, and verified OLAP execution.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.29668 [cs.LG]

(or arXiv:2609.29668v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.29668 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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