When you have a flow well defined, like transactions going on, it simply doesn't scale. But AI can then be used for analysis, alerts and investigating failures of such processes very nicely. Agents can also be used to prepare a transaction package that needs more human input, like a customer service case, but again with clearly defined outcomes. At least that's what I've seen in my limited experience consulting for a local online retailer.

I tend to draw the line at automating the LLM to respond to things. If it's responding to some sort of external source, that source is usually somewhat consistent to the point I'd rather have the LLM create a script to parse the data and do that automatically. I've got a job search tool that I built recently using Claude Code. CC created scripts to scrape certain websites and scheduled them using native OS schedulers. The results get parsed and dropped into a sqlite database. No LLM is involved in the automated portion of this process. I've got some general status scripts which push details about the current health state of my servers and apps and also will alert me when job listings reach some defined threshold. At that point I use the LLM to look through the new jobs and categorize them based on work I'd find interesting giving me a prioritized list.

If all LLM tools disappeared tomorrow, all of my scripts and processes developed with an LLM will continue to work without hiccup. If anthropic went out of business tomorrow, I'd lose nothing switching to another provider because I don't have to "trust" agentic operations in automated processes. They are always overseen by me and they are rarely creating things I couldn't have created myself. It's just much faster to iterate on it with these tools.