Most beginners only ask if an AI workflow works. The better question is where it wastes time and money.
One builder shared an n8n loop that watches a task board, reads the logs, asks Claude to write a fix, lets Codex review it, opens the pull request, deploys, and repeats. The same setup was used across six client retainers.
Across 139 runs, the builder reported finding 8.6 million wasted tokens in steps whose output nobody used. That is a useful warning for anyone building automations for clients.
- Break the workflow into named steps.
- Log the input, output, time, and model cost for each step.
- Add a second review model before code reaches a pull request.
- Remove steps whose output is never read by another step.
- Keep a human approval point before deployment.
Read the original workflow report here: https://x.com/troygentic/status/2095561914969428138
If u are building client automations, the AI Marketplace is also a useful place to compare practical resources: https://coding180.com/ai-marketplace
Would u rather optimize the model prompt first, or measure every workflow step before changing the prompt?
