Shadow Mode
A system deployment strategy in which a new policy or model runs in parallel in the background and records its decisions without affecting actual business operations. It is used to evaluate policy effectiveness with zero risk and is widely applied in machine learning model deployment and AI agent policy validation.
Core Facts
Timeline (last 90 days)
Agents should first go live in suggestion-only mode to generate drafts, with reasons for reviewers' edits or rejections recorded, before gradually automating low-risk requests.
Common strategies for output quality gating include rule-based heuristic detection, real-time scoring using LLM-as-Judge, and Shadow Mode, which compares response differences between the new and old models
Shadow Mode refers to a setup in which the new version of the system receives the same real requests as the old version, executes them independently and records the output, but does not return the results to users.
In Shadow Mode, the Agent should not actually execute write-operation tools (such as sending emails or modifying databases), otherwise unintended business impacts will occur.
Shadow Mode was first widely used in the deployment process for machine learning models, and is also known as Dark Launch or Shadow Deployment.
The advantage of Shadow Mode over offline testing is its ability to capture the distribution characteristics of real production traffic, including edge cases that developers did not anticipate.
Shadow Mode allows policies to only record decisions that should have been intercepted or permitted without actually intervening in the agent's real behavior, thereby evaluating policy effectiveness under zero-risk conditions.
All Facts (7)
Shadow Mode refers to a setup in which the new version of the system receives the same real requests as the old version, executes them independently and records the output, but does not return the results to users.
75%UnverifiedAgents should first go live in suggestion-only mode to generate drafts, with reasons for reviewers' edits or rejections recorded, before gradually automating low-risk requests.
50%UnverifiedCommon strategies for output quality gating include rule-based heuristic detection, real-time scoring using LLM-as-Judge, and Shadow Mode, which compares response differences between the new and old models
50%UnverifiedIn Shadow Mode, the Agent should not actually execute write-operation tools (such as sending emails or modifying databases), otherwise unintended business impacts will occur.
50%UnverifiedShadow Mode was first widely used in the deployment process for machine learning models, and is also known as Dark Launch or Shadow Deployment.
50%UnverifiedThe advantage of Shadow Mode over offline testing is its ability to capture the distribution characteristics of real production traffic, including edge cases that developers did not anticipate.
50%UnverifiedShadow Mode allows policies to only record decisions that should have been intercepted or permitted without actually intervening in the agent's real behavior, thereby evaluating policy effectiveness under zero-risk conditions.
50%