Robo-Reporters: Evaluating Autonomous AI Agents as Algorithmic Gatekeepers in Computational Journalism

Q: What is the main research gap addressed by ROBO-REPORTERS?

The study targets the lack of empirical comparisons across different AI agent architectures in journalism, instead of treating AI as a single monolithic approach.

Q: How were the four AI agent architectures evaluated?

The authors run 200 controlled experiments across 50 journalism tasks of graduated difficulty, holding the underlying language model and tools constant to isolate architectural effects.

Q: Which architecture achieved the best accuracy and what trade-off did it show?

Multi-agent collaborative agents achieved the highest accuracy (84.7%) but required roughly twice the time cost compared with the other designs.

Jul 16, 2026

Upload by Rowan

Artificial intelligence agents increasingly perform journalism tasks autonomously, including searching for sources, evaluating credibility, and drafting news with limited human oversight. Research has largely treated AI as a single category, leaving the impact of agent architecture untested. Using gatekeeping theory, a controlled comparison of four architectures—monolithic, chain-based, multi-agent collaborative, and autonomous iterative—runs 200 experiments across 50 journalism tasks. Architecture significantly affects runtime and processing strategy (η2 up to 0.82); multi-agent reaches 84.7% accuracy at about twice the time cost. Architecture also shapes transparency and filtering behavior, positioning design as a structural gatekeeping layer.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

Introduction

Research gap and motivation

Gatekeeping theory and transparency lens

Research questions

ROBO-REPORTERS: EVALUATING AUTONOMOUS AI AGENTS AS ALGORITHMIC GATEKEEPERS IN COMPUTATIONAL JOURNALISM Obada Kraishan College of Media and Communication Texas Tech University Lubbock, TX 79409, USA Kulsawasd Jitkajornwanich College of Media and Communication Texas Tech University Lubbock, TX 79409, USA arXiv :2607 . 10736v1 [ cs .CY] 12 Jul 2026 Kerk Kee College of Media and Communication Texas Tech University Lubbock, TX 79409, USA July 14, 2026 ABSTRACT Artificial intelligence agents increasingly perform journalism tasks autonomously, searching for sources, evaluating credibility, and producing news content with minimal human oversight. Yet research has largely treated AI as a monolithic category, leaving the effects of architectural design unexamined. Drawing on gatekeeping theory, this study presents the first systematic comparison of four agent architectures, monolithic (Claude), chain-based (LangChain), multi-agent collaborative (CrewAI), and autonomous iterative (AutoGPT), across 200 controlled experiments spanning 50 journalism tasks of graduated difficulty. All architectures used the same underlying language model and identical tools, isolating architectural effects. Results revealed significant effects on task duration (F(3, 196) = 24 .54, p < .001, η 2 = .27) and computational strategy (F(3, 196) = 305 .63, p < .001, η 2 = .82), with architecture explaining 82% of the variance in processing behavior. Multiagent collaboration achieved the highest accuracy (84.7%) at roughly twice the time cost of other designs. Multistage analysis of the monolithic architecture documented a 71.7% source rejection rate, a quantitative parallel to classic human gatekeeping, while framework-based systems obscured their filtering inside abstraction layers. Transparency emerged as an architectural choice: framework designs excelled at structured attribution, whereas monolithic and iterative designs produced superior methodological documentation. Findings position architecture as a new structural level of gatekeeping and offer evidence-based guidance for newsrooms: chain-based designs for speed, multi-agent for accuracy, monolithic for versatility, and iterative for auditability. Keywords artificial intelligence · autonomous agents · computational journalism · gatekeeping theory · algorithmic transparency · journalism automation 1 Introduction Over the past decade, artificial intelligence has changed journalism by growing from automating routine tasks to developing systems that independently make editorial decisions, such as selecting newsworthy stories and crafting narratives [1, 2] . Contemporary AI agents can plan information-gathering strategies, dynamically evaluate sources, and iteratively refine outputs with minimal oversight [3, 4, 5, 6] . These capabilities position AI agents as genuine participants in the editorial process, fundamentally altering gatekeeping in contemporary journalism. The architecture of an AI agent (its fundamental design pattern for processing information and making decisions) determines how an agent searches for information, which sources it consults, and eventually what information reaches A PREPRINT-JULY 14, 2026 audiences [7, 8, 9] . Despite the significance of architectural variations, mass communication research has generally overlooked them, resulting in newsrooms adopting technologies without empirical guidance on their editorial implications. This study addresses that gap through a controlled experimental comparison of four architectures representing major design paradigms: monolithic, chain-based, multi-agent collaborative, and autonomous iterative. This gap has become urgent given the diversity of agent architectures now available to news organizations. Contemporary systems range from monolithic models that handle all reasoning internally to complex multi-agent configurations in which specialized components collaborate on information gathering and synthesis [8,

FAQs

What is the main research gap addressed by ROBO-REPORTERS?

The study targets the lack of empirical comparisons across different AI agent architectures in journalism, instead of treating AI as a single monolithic approach.

How were the four AI agent architectures evaluated?

The authors run 200 controlled experiments across 50 journalism tasks of graduated difficulty, holding the underlying language model and tools constant to isolate architectural effects.

Which architecture achieved the best accuracy and what trade-off did it show?

Multi-agent collaborative agents achieved the highest accuracy (84.7%) but required roughly twice the time cost compared with the other designs.

Jul 16, 2026

Upload by Rowan