Look at the AI visibility tools. They all integrate with multiple LLM models, include scheduling, management of multiple external processes, data parsing, site-scraping, graphs, as well as multiple database structures. They need retry and error logic, real-time displays and updates, and multiple flow UX's, and Stripe integration with webhooks, and subscription management.
Same thing with competitor monitoring. These tools require scraping multiple sites, checking X, Facebook, Jobs sites, Crunchbase, etc, aggregating data and displaying and making sense of changes. And the same multi-process management, queuing, and Stripe integrations.
A few years ago, these would both fit into businesses requiring many months of development to get it all running. Now we are seeing dozens of companies emerging in each of these categories each month as they take weeks to build. And if one finds a cool aha (a new integration or graph or UX flow or positioning) the others can quickly follow in a week or less of AI-agent coding.
There are dozens of other categories where this is happening too.
The hard part of figuring out the nuances of the APIs and integrations and retries and AWS integrations and Rabbit MQ configurations and corner cases can all be done by AI with the right context.