I like the decision diagram :)

The callout on enterprise automation is interesting b/c it's one of the $T sized opportunities that matters most here, and while I think the article is right in the small, I now think quite differently in the large for what ultimately matters here. Basically, we're crossing the point where one agent written in natural language can easily be worth ~100 python scripts and be much shorter at the same time.

For context, I work with teams in operational enterprise/gov/tech co teams like tier 1+2 security incident response, where most 'alerts' don't get seriously investigated as underresourced & underautomated teams have to just define them away. Basically every since gpt4, it's been pretty insane figuring this stuff out with our partners here. As soon as you get good at prompt templates / plans with Claude Code and the like to make them spin for 10min+ productively, this gets very obvious.

Before agents:

Python workflows and their equivalent. They do not handle variety & evolution because they're hard-coded. Likewise, they only go so far on a task because they're brain dead. Teams can only crank out + maintain so many.

After agents:

You can easily sketch out 1 investigation template in natural language that literally goes 10X wider + 10X deeper than the equiv of Python code, including Python AI workflows. You are now handling much more of the problem.