Retool Agents has arrived: 100 million hours already automated, and we’re just getting started
The AI industry has invested $1 trillion to build the most sophisticated reasoning engines in human history. Yet today, we use them as glorified writing assistants — copy-pasting their output into the actual systems where work happens. This isn’t AI’s failure. It’s ours.
We’ve created a bizarre workaround: humans acting as the application layer for AI. A few breakthrough tools have solved this — developers use Github Copilot and Cursor to let AI code directly, for example. But once you step outside the islands of software development, it’s an ocean of copy and paste. Humans today spend an inordinate amount of time ferrying data between LLMs and their actual work tools.
When you’re copying and pasting in and out of ChatGPT, you’ve become the API.
The problem is LLMs are not fully equipped to perform knowledge work. Knowledge work follows a universal pattern: gather information, analyze it, act on the results. LLMs have mastered analysis — they can architect solutions to complex problems with superhuman speed. But they're blind and paralyzed. They can’t see your customer data, can’t access your inventory systems, and can’t execute your business processes.
This creates an absurd situation. We have LLMs that can write perfect SQL queries but can’t connect to your database to run them. We have LLMs that can reason and work faster than any human can, but are held back by the fact that they can’t actually connect to anything.
The result? A $1 trillion investment in thinking machines that require human clerks to do their typing.
What’s missing: an application layer for AI
The solution isn’t better models — it’s giving LLMs eyes and hands. With the right application layer, LLMs transform from advisors to workers. They don’t just analyze copied-and-pasted data; they directly retrieve it from your systems. They don’t just suggest actions; they execute them.