How to Shadow-Test an AI Agent Without Letting It Act Twice
You have a new prompt, model, or orchestration policy. Offline tests look good, but the change will see vocabulary, timing, and context combinations that your fixture set never captured.
So you shadow-test it: send production traffic to both the current agent and the candidate, compare their decisions, and keep the current agent in control.
Then the candidate calls send_email too.
Now one user request has produced two real messages. The “safe” experiment has become a duplicate-action incident.
This synthetic failure happens when shadowing is implemented as “run the whole application twice.” For agents that can change the world, shadow testing must duplicate observation and decision—not authority and effects.
The Architecture Boundary That Makes Shadowing Safe
An action-taking agent contains at least four separable stages:
input -> decision proposal -> policy/approval -> tool execution -> outcome
The shadow should stop after the proposal and policy simulation:
+--> primary decision --> real...
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