Most n8n AI workflow tutorials end at "it worked in testing." The gap between a demo and a production system handling 10,000 items/day with real money on the line is where the interesting problems live.
At Chronexa, we've built 50+ multi-agent workflows for fintech compliance teams, legal document processing, AI SDR engines, and RAG-powered research assistants. Here's what we've learned about making them reliable.
1. Design Failure as a First-Class Concern
Most n8n tutorials wire main[0]\. Production workflows wire main[0]\ and main[1]\.
Every HTTP Request node and AI node has two outputs in n8n: success (main[0]\) and error (main[1]\). Leaving the error branch unwired means failures disappear silently — you only find out when a client notices something is wrong three days later.
