Agents now compete on cheap human steering, not on generation.编码智能体如今比拼的是“让人低成本地掌舵”，因为限制产出的是人的监督注意力，而不是生成能力。

The default read of every coding-agent release is "smarter model, faster work." OpenAI's latest Codex update points elsewhere: at the minutes a person spends watching, redirecting and re-approving the agent.

On Oct 15, OpenAI shipped Codex CLI 0.163.0. A follow-up message can now redirect Codex mid-response, and instant interrupts are on by default.

Independent read-only lookups run in parallel. A resumed session restores its permissions and network approvals. None of this makes the model smarter; each change cuts the cost of supervising it.

Why spend a release on that? Because the measurements keep pointing at the human. In METR's 2025 trial, 16 experienced open-source developers took 19% longer on 246 real tasks with AI allowed, yet believed afterwards they had been 20% faster.

METR's February 2026 follow-up estimated an 18% speedup for returning developers, with a range wide enough to include a slowdown. METR called it very weak evidence, and said time tracking became unreliable once developers ran several agents at once.

A separate METR note on 5,305 Claude Code transcripts put a soft upper bound of 1.5x to 13x on time savings, and found more concurrent agents associated with bigger savings.

You can launch four agents at once and still redirect them only one at a time. Generation runs in parallel. Your attention to redirect it does not.

That moves the product edge to interrupt latency, resumable permissions and parallel lookups, because vendors gain when one person can keep more agents running. The second-order effect cuts both ways: permissions that survive a resume make supervision cheaper, and make an over-broad approval last longer.

The strongest counter: autonomy is rising. METR's time-horizon trend shows agents finishing ever-longer tasks unaided.

On clean, well-specified work with tests, you can launch and walk away, and model capability does dominate. But METR also reports agents doing worse on messier tasks, and most work inside real repositories is messy.

If you run more than one agent, try this for a week: log interrupts per run and minutes to an accepted change. If supervision minutes dominate, fix that before paying for a bigger model.

We'd revise this if an independent study by mid-2027 shows time per accepted change falling with model upgrades alone, while interventions per task stay flat.