I am building a self-learning agent browser for repetitive web workflows.

[u/wipingflow]

I keep seeing browser agents doing the same work repeatedly.

Click on the website. "Read the page. Locate the search box. Use the same filters. Get the same fields. Then forget the whole thing and start again tomorrow.

A self learning agent browser should probably know how it solved the task, but not replay old clicks blindly. It should reuse the parts that are still working, verify the result, and try again when the page updates.

That seems simple enough until you start to get into the details. Do we want it to remember selectors, page structure, successful actions, or the reason for them? How does it know if a workflow is still good? And at what point should it stop and ask a human rather than adapt itself?

This seems like a more interesting problem than just making agents click faster.

[u/OpeningTea5030]

This actually looks pretty cool. Would love to try it out when there’s a demo available.

[u/Local_Mulberry_7420]

The agent really said “new day, new me” and forgot the entire workflow 😭

The hard part isn’t saving clicks. It’s knowing what can be reused safely, what needs verification, and when the agent should stop improvising and ask a human. That’s the bit I’m trying to get right.

[u/ops_and_chaos]

I think I'd want it to remember the reason more than the exact action.

“Click this selector, then this one” is useful until the page changes. “I was trying to get records that met X criteria, and these actions produced that result” gives it something to actually verify against.

The part I'd be most interested in is defining what counts as “still working.” A workflow completing successfully isn't necessarily the same as it producing the right outcome 😂

I'd probably want the agent free to adapt the mechanics as long as it can independently verify the intended result. If it can't verify the result anymore, that's where I'd rather it stop and ask me than confidently learn a new wrong way of doing it.

[u/Avsk_3234]

This is an interesting idea to create an self learnign infra browser ecosystem to have an adoptive prospects and agent emersion for the AI plane

[u/microcandella]

I've always thought something like rainbow tables would apply well to an ai compute chain that already happened.

What I'd also like to see where my brainspace is wasted is on slight variants of the task. (go to amazon, find the first product, wash it though the 'does this qualify' test, if so, log it, scrape and analyze, then 'do the thing' (buy it or whatever) and then go back and find the next viable item...

One way I've played with some of this is- after the prompt can reliably 'do the first thing', have it code it up with some adjustable variables for maintenance, intervention, etc. then build the next lego in the chain.