I built a lead-research pipeline and deliberately did not use an AI Agent.

I built a production-style prototype for researching and qualifying marketing agencies in Bengaluru using n8n. The workflow: Accepts a niche, location, and company limit Searches with Tavily Removes directories and duplicate domains Fetches and cleans website content Uses ScrapeGraphAI when normal extraction fails Uses OpenRouter for structured lead analysis Scores leads from 0–100 Stores results in Google Sheets Generates a Markdown report Logs failures through a separate Error Handler workflow One design decision I found interesting: I used a deterministic pipeline with a Basic LLM Chain instead of an AI Agent. The AI analyzes the company, but JavaScript enforces the score boundaries and qualification rules: 80–100: High 60–79: Medium 0–59: Low I chose this because lead scoring should be predictable and auditable. An agent could introduce unnecessary tool-selection and execution variability. What would you improve? Would you use an AI Agent for this workflow, or keep it as a controlled pipeline? GitHub link in the comments: