AgentWeb

AI agents that need to complete real tasks on websites, such as ordering, booking, submitting forms, or checking out, typically rely on browser automation tools like Playwright or computer-use agents. The text describes this approach as slow, fragile, and unverified, since it depends on controlling a browser and lacks structured confirmation that an action actually succeeded. Agents also have no standard way to know which sites and actions are actually supported, what permissions are required, or whether a result can be trusted, which makes promising task completion risky.

AgentWeb indexes real websites into maintained Action Maps of callable, verified actions that AI agents can execute through MCP or API instead of driving a browser. Agents connect to the AgentWeb MCP server, explore anonymously with read_page and discover, then use a claim-link sign-in flow to get an aw_ API key before calling list_actions and execute. Each supported action declares its inputs, scoped permissions, authentication requirements, and result contract, and every execution returns structured, typed data plus a verification receipt. Target-site credentials are vaulted through a hosted sign-in ceremony and never exposed to the agent, and payments are metered per verified action with automatic refunds on failure.