Evaluate AI Agent Tool Usage Accuracy with Evaluation Nodes

This workflow is designed for AI developers building multi-agent systems in n8n. It quantitatively assesses whether AI agents correctly select and use expected tools by comparing actual tool calls (e.g., web search, calculator, vector search, summarizer) against predefined ground-truth datasets. Triggered via chat input or dataset rows, it routes through a multi-tool agent powered by top LLMs, extracts intermediate steps (action + observation), and uses Evaluation Trigger and nodes to score performance as pass/fail.

Key benefits include plug-and-play observability for agent behavior, dataset-driven testing, and persistent logging to Google Sheets for easy analysis and iteration. It helps verify autonomous agent decisions, reducing debugging time and improving reliability in complex AI workflows.

Ideal use cases: Testing agentic AI in production-like scenarios, benchmarking LLMs on tool selection, and scaling multi-agent evaluations. Setup involves Google Sheets integration, model/tool configuration, and Qdrant vector store population with sample data.