An insurer wants to replace the model that flags suspicious claims. For two weeks, every claim goes to both models. The current one still makes the decision; the new one’s verdict is only logged. Afterward, the team reviews where the two disagreed, checks a sample by hand, and compares speed and cost. No customer was affected by the new model’s mistakes.
Shadowing answers questions that offline evals can’t: how the new version handles real traffic volume, today’s mix of requests, and odd inputs nobody put in a test set. It doesn’t show how users would react to the new answers. That needs a canary deployment or an A/B test, which usually come next. Shadowing also doubles inference cost while it runs, so teams often mirror only a share of traffic.
Agents need extra care. A shadow copy of an agent that can send emails, issue refunds or write to a database must not actually do those things, or the “invisible” version has real effects. Stub or sandbox its tools, record the actions it would have taken, and compare those plans against what the live agent did. Some cloud providers call the feature traffic mirroring. “Shadow mode” also has a looser meaning, an AI working alongside staff whose suggestions are reviewed but not acted on, which is closer to human in the loop. It is unrelated to shadow AI, the unsanctioned use of AI tools at work.
Sources
- Amazon SageMaker AI docs: Shadow tests — Deploys a new variant in shadow mode, routes a copy of inference requests to it in real time, and returns only the production variant's responses; shadow responses can be logged for offline comparison; checked October 24, 2026.
- Microsoft Learn: Safe rollout for online endpoints (Azure Machine Learning) — Describes mirroring a percentage of live traffic to a new deployment to validate it before sending it live traffic; checked October 24, 2026.