Measuring AI agent autonomy in practice

This post empirically measures AI agent autonomy in practice by analyzing millions of human-agent interactions across Claude Code and the public API. It quantifies how much autonomy users grant agents, how this changes with user experience, the domains agents operate in, and the risk associated with agent actions. Key findings include increased autonomous session length in Claude Code, a shift towards more auto-approvals and interruptions by experienced users, and the observation that agents are used in risky domains, though not yet at scale. The post also details the methodology for studying agents, distinguishing between individual tool calls on the public API and full workflows in Claude Code, and highlights the need for new post-deployment monitoring infrastructure and human-AI interaction paradigms.