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Human steering is every moment a person has to step in mid-task to redirect an agent — clarifying an ambiguous instruction, pointing it at the right document, approving a step it should have taken itself, or nudging it past a point where it stalled. A little is fine. A lot is the difference between an agent that saves time and one that just relocates the work.

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Why steering is the real bottleneck

When human steering is required for a task, it caps the autonomy, speed, and value the agent delivers inside the workflow the human was trying to hand off. You wanted to delegate the task; instead you’re babysitting it. Three costs stack up:
  • Throughput — the agent runs only as fast as a human can attend to it.
  • Scale — one operator can supervise a handful of agents, not hundreds.
  • Trust — a system that needs constant correction never earns the autonomy to run unattended, so it stays stuck at “supervised.”
The value of agents shows up specifically when you cross from steered to autonomous. See the autonomy spectrum.

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Where steering comes from

Most steering traces back to a handful of root causes — and notably, most are context and harness problems, not model problems:

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How to reduce it

1

Make discovery the agent's job

The most common steering — “you’re looking at the wrong thing” — disappears when the agent searches structured context itself instead of relying on a human (or a guessed pre-injection) to hand it the right document.
2

Contain drift at the data model

Structured records and typed edges stop the agent wandering into unrelated instructions, which removes a whole class of “why did it do that?” interventions.
3

Let the harness handle the hard step

Soft gates keep it from exiting early on fuzzy tasks; critic sub-agents catch bad reasoning before a human has to. The agent recovers on its own instead of waiting for a nudge.
4

Give the human a reason not to intervene

Provenance lets the human verify after the fact instead of supervising during it — moving them from the inner loop to the outer loop.
Reducing steering isn’t about a smarter model. It’s about removing the reasons a human has to intervene — most of which live in the context layer and the harness around the model.

Next: Determinism

Making the agent’s behavior repeatable enough to trust unattended.