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June 11, 2026Open Access

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

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Authors

MAMuawia Ali

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Overview

Comparative study assesses performance of multi-agent workflows in enterprise AI tasks using Gemini and n8n.

Key Points

  • This study aims to evaluate the effectiveness of different multi-agent workflow architectures for AI tasks in enterprise settings.
  • Empirical evaluation of three workflow architectures: Basic Agent, Planner Executor, and Planner Executor Reviewer.
  • Utilized Google Gemini-3.1-flash-lite and the n8n platform for the evaluation.
  • Conducted 90 experimental runs on a dataset of 30 enterprise-oriented tasks across various categories.
  • Multi-agent workflows achieved higher confidence scores than the single-agent baseline.
  • Improved performance noted in reasoning-intensive and challenging tasks.
  • Workflow architecture significantly influences AI performance and consistency.

Cite This Study

Muawia Ali (2026) studied this question.

synapsesocial.com/papers/6a2a533380c8f91e7f39ed63https://doi.org/10.5281/zenodo.20606083
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