AI Agents Are Ready — We're the Ones Who Aren't

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Reader, there’s probably not a single day these days when you don’t hear the phrase “AI agent.” AI that writes code for you, sorts your emails, even executes financial transactions. But almost no research has actually measured how much work people are handing over to these agents in practice.

In February 2026, Anthropic published a study analyzing millions of human-agent interactions across its coding agent Claude Code and its public API. The title is “Measuring AI Agent Autonomy in Practice.”

What makes this study interesting is that it doesn’t measure “how smart AI has become,” but “how much people are actually delegating to it.” And the results are quite telling. Today I want to use this study as a jumping-off point to talk about where the real bottleneck sits in the age of AI agents.

What 45 Minutes Means: Autonomous Working Time Has Doubled

Let’s start with the core data from Anthropic’s study. They measured how long Claude Code works on its own without human intervention — what’s called “turn duration.”

Most turns are short. The median is about 45 seconds, and that number has barely moved over several months. As new users keep flowing in, the overall average has stayed stable.

The really interesting part is the tail data. The longest sessions — the top 0.1% of turn durations — nearly doubled, from under 25 minutes to more than 45 minutes, between October 2025 and January 2026. In just three months.

What’s worth noting here is that this increase happened gradually, independent of new model releases. If autonomy were purely a function of model capability, you’d expect sharp jumps every time a new model shipped. But the graph traces a smooth upward curve instead. This suggests power users grew increasingly comfortable trusting the tool over time, and began handing it progressively more ambitious tasks.

Anthropic calls this phenomenon “deployment overhang” — the idea that the autonomy a model can actually handle is far greater than the autonomy it’s actually given in the real world.