Stop Writing Selectors: How Shared Intelligence Makes Browser Automation Self-Improving

Most browser automation breaks because every script starts from zero. It finds a button, clicks it, the site redesigns, it breaks. Repeat forever. After building browser automation for AI agents, we noticed a pattern: every agent re-learns the same websites independently. Agent A figures out how to search on Amazon. Agent B does the same work an hour later. The knowledge is generated and immediately discarded. The Idea: Collective Agent Memory What if agents pooled their browsing knowledge? We built a shared intelligence layer where every agent interaction with a website contributes verified execution paths back to a collective knowledge base. The pattern is simple: Browse — Check what's known about a domain Execute — Get a pre-verified plan (or explore if unknown) Report — Feed back what worked and what didn't When Agent B visits a site Agent A already mapped, it gets a structured execution plan instead of raw-dogging the DOM with screenshots. What We Observed After running this in pr