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Shadow mode: how to earn autonomy for an AI agent

Shadow mode: how to earn autonomy for an AI agent

A step-by-step playbook for running an agent alongside your team until its decisions are good enough to trust on their own.

A step-by-step playbook for running an agent alongside your team until its decisions are good enough to trust on their own.

Portrait of Rhea Natarajan

Rhea Natarajan

7 min read

Abstract data illustration for “Shadow mode: how to earn autonomy for an AI agent”

Shadow mode means the agent does everything except the final action. It reads the ticket, does the research, makes a decision and writes down what it would have done — while your team keeps working as usual.

Week one: watch

Pick one queue and one decision type. Run the agent on every item and compare its proposal with what your team actually did. Don’t tune anything yet; you’re collecting a baseline.

Week two: correct

Every disagreement becomes an eval case. Most fixes are boring — a missing policy rule, an ambiguous field, a tool that needs one more permission. Fix those before touching prompts.

  • Track agreement rate per decision type

  • Tag disagreements by cause: policy, data, tool or reasoning

  • Re-run the full eval set after every change

Week three: switch on narrowly

When agreement for a decision type stays above your bar — most teams use 95% — turn on autonomy for that decision only. Keep everything else in shadow mode, and keep reading a sample of autonomous runs.

Autonomy isn’t a launch. It’s a dial you turn one notch at a time.

Portrait of Rhea Natarajan

WRITTEN BY

Rhea Natarajan

Head of Agent Operations

Rhea leads the team that helps customers move agents from shadow mode to full autonomy. Before Cairn she managed a 200-person support org.

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