Evaluating No-Code Agentic AI Frameworks for Enterprise Automation: A Practical Comparison of Make.com and n8n.io

The rapid adoption of Agentic Artificial Intelligence
across enterprises has created demand for accessible automation
platforms that do not require traditional programming expertise.
No-code workflow tools such as Make.com and n8n.io have
emerged as practical solutions; however, peer-reviewed literature
directly comparing these platforms for Agentic AI automa-
tion remains scarce. This paper addresses that gap through a
practitioner-led comparative study. Three functionally identical
Agentic AI workflows — an AI travel planning assistant, an
automated stock market alert system, and an AI search quality
evaluator — were independently implemented on both Make.com
and n8n.io and evaluated across seven criteria. Findings indicate
that n8n.io demonstrated superior execution speed, fewer build-
time errors, higher AI accuracy, stronger debugging feedback,
and greater cost efficiency through its credit-free model, while
Make.com offered advantages in public workflow sharing and
beginner accessibility. The study concludes that platform selection
should be guided by specific project requirements rather than a
single universal recommendation, and identifies the absence of
native fine-tuning and Retrieval-Augmented Generation support
as a shared limitation of both platforms.