How We Cut Browser Agent Costs 7,000x with Collective Intelligence

Every browser agent does the same expensive thing: dump the entire DOM into an LLM, ask "what should I click?", and repeat for every step. A 10-step workflow costs ~$4 in LLM tokens and ~50 seconds in reasoning time. Multiply that by every agent, every session, every day — and you're burning cash on knowledge that already exists. The core problem: amnesia at scale When Agent A figures out how to search flights on WebsiteA, that knowledge evaporates when the session ends. Agent B starts from scratch. So does Agent C. Every agent pays full price to re-learn what hundreds of agents have already discovered. This is the browser automation equivalent of every developer rewriting left-pad from scratch, every time, in every project. What if agents could share what they learn? We built AIR SDK — an open-source MCP server that maintains a shared capability graph across all connected agents. Three API calls: browse_capabilities(domain) → What actions can be performed here? execute_capability(acti