Shadow mode is a way of running an AI agent on real, live work while its outputs are recorded but never used: people continue to handle every case, and the agent's decisions are compared with theirs afterwards. It shows how the agent would have performed in your operation, at no risk to customers.
Why does shadow mode matter?
Tests on past cases show whether an agent handles the situations you thought to include. Live work contains the ones you did not: new phrasings, odd documents, combinations nobody anticipated. Shadow mode exposes the agent to that full mix without letting any of its mistakes reach a customer.
It also builds the case for authority. Comparing the agent's shadow decisions with what experienced staff actually did gives evidence the operation trusts, because it comes from its own cases, handled by its own people, in the same period.
How does shadow mode work in practice?
- Connect read-only. The agent receives the same inputs as staff but cannot write to any system or send anything.
- Record its outputs: the action it would have taken, the draft, the facts it relied on and their sources.
- Compare with what staff did, case by case. Agreements are evidence; disagreements need review.
- Classify disagreements: agent wrong, staff wrong, both acceptable, or case outside the agent's role.
- Decide against exit criteria agreed in advance.
How to run an AI agent in shadow mode covers the set-up in detail, and the shadow run report template gives a format for the results.
What does an example look like?
A fraud operations team plans an agent to triage card fraud alerts. For four weeks it runs in shadow: each alert goes to analysts as normal, and the agent records whether it would have closed the alert, asked the customer to confirm, or escalated. At the end, the team reviews every disagreement. The agent was too quick to close alerts on accounts with recent address changes, and more cautious than analysts on small online purchases. The brief is adjusted, and the agent goes live at depth 2, drafting dispositions for analysts to confirm.
How is shadow mode different from testing?
Testing replays a fixed set of past cases with known answers. Shadow mode runs on today's work, as it arrives, alongside people doing it for real. Testing is repeatable and fast; shadow mode is slower but shows the agent in the actual mix of your operation. Certification gives the agent its depth; shadowing shows the operation what that depth will feel like in practice. The case for shadowing before go-live is made in shadow your AI agent before it goes live.
It also differs from A/B testing, where both versions act on real cases. In shadow mode, nothing the agent produces takes effect.
What does shadow mode not prove?
It cannot show how reviewers will behave once the agent's drafts are in front of them, including trusting it too readily. Treat it as strong evidence about the agent's judgement, not about the whole process after go-live.
Which terms sit close to it?
Parallel run: an older term for the same idea, used in system migrations. Canary release: the first step where the agent's work counts. Certification: the formal evidence for depth. Note that shadow mode is unrelated to shadow AI, which means unsanctioned AI use. See our guide to deploying AI agents for where shadowing fits.
Where Pryme fits
Attest certifies by replaying the agent against real cases from the customer's own operation. Shadow mode adds live evidence on top: the same operation, the same week, compared directly with what staff did. Together they give the owner grounds for the depth they approve.
Questions people ask
How long should a shadow run last?
Long enough to cover the full cycle of your work, including peaks and month-end. For many processes that is two to six weeks.
Does shadow mode need customer consent?
The agent is processing customer data even though it does not act, so it needs a lawful basis and should appear in your records of processing. Specific consent is not usually the basis, but check with your data protection lead.