For context: I built a tool which allows users to screen record a task and the agent "loops" till it is able to build a deterministic script to complete the task end to end.

It infers the automation "goal" and a detailed context of the task from the screen recording. Then it loops and learns how to do the task by trial and error till it gets the complete automation right, end to end. It then compiles a deterministic script to rerun with a different set of inputs.

The loop that I built is the agent's access to bash + browser agent + cua agent + internet access. These tools allow the agent to iteratively try out different approaches till it figures out the one that works and it creates an automation script stably out of it.

This helps in 2 things:

Cost: the whole pushback on loops is "who can afford them?". Here the agent learns once and every future rerun is nearly free.

Context not prompting: Recording hands over the whole task up front, so you can skip the back and forth of iteratively prompting an agent.

What happens when the script breaks?

The agentic loop resumes. It passes the originally learnt context, script error logs to finish the task and heal the script when needed.

How are you building loops in your workflows?

My approach to building Steinberger's "loops" for computer automations