{"slug": "graph-loop-and-harness-engineering-for-zero-trust-agentic-data-engineering-and", "title": "Graph, Loop, and Harness Engineering for Zero-Trust Agentic Data Engineering and Analytical Processing", "summary": "A new arXiv paper (2609.29668v1) presents two zero-trust frameworks for large language model agents that automate cloud data workflows: Zero-Trust Agentic Data Engineering, which generates, deploys and verifies complete cloud data-engineering solutions from natural-language tasks with completion conditioned on repository, deployment, runtime and policy evidence, and Zero-Trust Agentic OLAP, which permits production promotion only after validation and evidence-bound approval and releases 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 — and are evaluated under nominal execution, controlled failures, bounded recovery and policy-constrained conditions, measuring verified completion, recovery, authorization enforcement, production promotion and verified OLAP execution.", "body_md": "arXiv:2609.29668v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/graph-loop-and-harness-engineering-for-zero-trust-agentic-data-engineering-and", "canonical_source": "https://www.machinebrief.com/news/graph-loop-and-harness-engineering-for-zero-trust-agentic-da-nydy", "published_at": "2026-09-25 04:00:00+00:00", "updated_at": "2026-09-25 06:00:40.101632+00:00", "lang": "en", "topics": ["ai-agents", "ai-safety", "large-language-models", "ai-research", "mlops"], "entities": ["arXiv", "Zero-Trust Agentic Data Engineering", "Zero-Trust Agentic OLAP"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/graph-loop-and-harness-engineering-for-zero-trust-agentic-data-engineering-and", "markdown": "https://wpnews.pro/news/graph-loop-and-harness-engineering-for-zero-trust-agentic-data-engineering-and.md", "text": "https://wpnews.pro/news/graph-loop-and-harness-engineering-for-zero-trust-agentic-data-engineering-and.txt", "jsonld": "https://wpnews.pro/news/graph-loop-and-harness-engineering-for-zero-trust-agentic-data-engineering-and.jsonld"}}