NORA: A Harness-Engineered Autonomous Research Agent for End-to-End Spatial Data Science

TL;DR

NORA is a domain-specific autonomous research agent designed for spatial data science, integrating specialized skills and harness engineering principles to enhance research efficiency and reproducibility.

Contribution

The paper introduces NORA, a harness-engineered multi-agent system with domain-specific skills tailored for GIScience, advancing autonomous spatial data science research.

Findings

NORA's domain-specific skills improve research efficiency.

Harness engineering enhances reliability and reproducibility.

Case studies show superior research quality with NORA.

Abstract

The automation of scientific research workflows has emerged as a transformative frontier in artificial intelligence, yet existing autonomous research agents remain largely domain-agnostic, lacking the specialized reasoning, method selection, and data acquisition capabilities required for rigorous spatial data science. This paper introduces NORA (Night Owl Research Agent), a harness-engineered, multi-agent autonomous research system purpose-built for GIScience and spatial data science. NORA orchestrates the complete research lifecycle through a skills-first architecture comprising 21 domain-specialized workflow skills, 9 specialist sub-agents, and custom Model Context Protocol (MCP) servers. Central to the system's design are two novel domain-specialized skills: a spatial analysis skill unit that encodes decision frameworks for exploratory spatial data analysis, spatial regression, and diagnostics; and a spatial data download skill that supports reproducible acquisition from authoritative geospatial data sources. We formalize the concept of harness engineering for scientific research agents, demonstrating how lifecycle hooks, safety gates, generator-evaluator separation, human-in-the-loop, and state persistence ensure reliable and reproducible autonomous research. We evaluate NORA through case studies by 6 domain specialists and 3 LLM reviewers across seven dimensions (novelty, quality, rigor, etc). Results demonstrate that domain-specialized harness engineering substantially improves the efficiency and quality of research output compared to general-purpose agent configurations.