Evaluating Multi-Agent Workflow Architectures for Enterprise AI Tasks: A Comparative Study Using Gemini and n8n

Evaluating Multi-Agent Workflow Architectures for Enterprise AI Tasks: A Comparative Study Using Gemini and n8n

Author: Muawia Ali
Role: Independent Researcher
Publication Date: June 9, 2026
Publication Type: Research Paper / Preprint
DOI: 10.5281/zenodo.20606084

This publication presents an empirical evaluation of enterprise-focused agentic workflow architectures using Gemini and n8n, with a comparative analysis across single-agent and multi-agent designs.

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Abstract

This paper presents an empirical evaluation of three agentic workflow architectures for enterprise-oriented AI tasks: Basic Agent, Planner Executor, and Planner Executor Reviewer. The study was conducted using Google Gemini-3.1-flash-lite and the n8n workflow automation platform. A dataset of 30 enterprise-oriented tasks covering Knowledge, Reasoning, and Coding categories was evaluated across all workflow architectures, resulting in 90 experimental runs. The findings indicate that workflow architecture has a measurable impact on AI performance and consistency. Multi-agent workflows achieved higher confidence scores and demonstrated improved performance on reasoning-intensive and hard tasks compared to a single-agent baseline.

Research Highlights

  • Evaluated three agentic workflow architectures
  • Conducted 90 experimental runs
  • Compared single-agent and multi-agent approaches
  • Used Gemini and n8n workflow automation
  • Measured confidence and task performance
  • Used an enterprise-oriented evaluation dataset

Citation

APA Citation

Ali, M. (2026). Evaluating Multi-Agent Workflow Architectures for Enterprise AI Tasks: A Comparative Study Using Gemini and n8n. Zenodo. https://doi.org/10.5281/zenodo.20606084

BibTeX

@misc{ali2026agentic,
author = {Muawia Ali},
title = {Evaluating Multi-Agent Workflow Architectures for Enterprise AI Tasks: A Comparative Study Using Gemini and n8n},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.20606084},
url = {https://doi.org/10.5281/zenodo.20606084}
}

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