AI Agent with Dynamic Model Selection Using n8n - 10x Cheaper Implementation

Learn to build a no-code AI agent system in n8n that automatically selects the optimal language model for each task, reducing costs by 10x while maintaining high performance. Discover how to create an intelligent routing system that dynamically chooses between different AI models based on whether the request involves simple conversation, research-heavy questions, function calls, or complex logical reasoning. Explore the complete workflow setup including Slack integration triggers, AI model selection logic, dynamic brain agent configuration, and output logging systems. Master the use of LLM comparison tools like Vellum AI Leaderboard and LM Arena to evaluate model performance, and examine practical examples including reasoning model tests, OpenRouter's automatic selection features, and RAG (Retrieval-Augmented Generation) AI agent implementations that demonstrate real-world cost optimization strategies.