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Chapter 4 · 2025

Agentic generative AI for context-aware outlier removal and historical cost optimization in construction

Maria Garcia, David Lee, Sophia Chen

Abstract

This paper explores the application of agentic generative AI for optimizing historical cost data in the construction industry. It focuses on developing context-aware outlier removal techniques to improve the accuracy of cost estimations. By refining cost data through agentic AI workflows, the research aims to enhance decision-making and achieve significant cost savings in construction projects.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper directly addresses one of the core structural challenges in Zone III deployments. The research on Agentic AI, Cost Optimization, Construction Industry provides evidence-based foundations that enterprise architects cannot ignore when designing long-horizon autonomous workflows. The findings challenge the assumption that a base language model — however capable — can handle the complexity of durable, governed, multi-step execution without explicit architectural intervention. For Zone III practitioners, this paper belongs in the required reading list.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

Agentic AICost OptimizationConstruction IndustryOutlier RemovalGenerative AI