Designing Agent-Native Automation in n8n: A Scalable Framework Integrating AI Agents, Multi-Agent Systems, and Retrieval-Augmented Generation

Abstract

This research introduces an intelligent multi-agent automation framework that integrates Retrieval-Augmented Generation (RAG) within a modular architecture to enhance adaptive decision-making and knowledge-driven task execution. The system achieved retrieval accuracy of 86.5%, decision correctness up to 67%, and maintained latency under 0.36 seconds. The proposed system embeds lightweight AI agents capable of sensing, reasoning, and acting autonomously within workflow environments. These agents interact through a Multi-Agent System (MAS) layer that supports coordination, task allocation, and consensus formation. The RAG layer combines knowledge retrieval from a vector database with context-aware generation using large language models, enabling agents to make informed and fact-based decisions. To address the limitations of static workflow systems, this study proposes a dynamic, agent-native architecture. Experimental evaluation demonstrates that increasing the number of agents and task rates improves throughput, adaptability, and reliability with minimal impact on latency. The system achieved high retrieval accuracy, decision accuracy, and robust fault recovery, validating its effectiveness for real-time intelligent automation in industrial and smart environments.

Introduction

This research proposes an Agent-Native Automation Framework for the n8n workflow platform that integrates AI agents, Multi-Agent Systems (MAS), and Retrieval-Augmented Generation (RAG) to enable adaptive, context-aware, and collaborative automation. Traditional workflow automation platforms like Zapier and n8n are rule-based and static, limiting their ability to respond dynamically to changing data or complex decision-making tasks. By embedding lightweight AI agents within n8n nodes, coordinating them via MAS, and enhancing reasoning with RAG (retrieving context from external knowledge sources for LLMs), workflows become intelligent, self-adaptive, and explainable.

The framework is modular, allowing agents to perceive, reason, and act autonomously, while MAS ensures coordination, fault tolerance, and scalability. RAG enhances decision-making by grounding outputs in factual knowledge, reducing errors. Use cases include IT support, academic workflows, and e-commerce automation, exemplified by a multimodal Telegram assistant that processes text, voice, and image messages, interacts with Google AI models, and manages tasks in real time. Overall, this approach transforms static workflow automation into dynamic, AI-driven, collaborative enterprise systems.

Conclusion

The proposed multi-agent automation framework demonstrated significant improvements in task handling efficiency and intelligent decision-making performance. Experimental results with varying numbers of agents and task rates revealed that increasing the task rate generally enhanced throughput, indicating better resource utilization and parallel execution efficiency. The latency values remained within an acceptable range (approximately 0.32–0.36 seconds), confirming that system responsiveness was maintained even under higher load conditions. Moreover, the system achieved consistently high retrieval accuracy (around 0.84–0.86) and AI decision accuracy (around 0.83–0.86), validating the reliability of the integrated intelligent agents. The fault recovery rate and success rate exceeded 90 percent across most configurations, showing the robustness of the framework against operational failures. Overall, the results prove that the multi-agent system can coordinate complex decision tasks with low latency and high stability, outperforming single-agent or sequential task execution methods. This architecture lays the foundation for globally scalable, explainable, and privacy-conscious intelligent automation systems.

References

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Copyright

Copyright © 2025 Vipin Kumar Vishwakarma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.