Glossary
Shadow Deployment
Running a new or modified AI agent in production environment alongside the current version, processing real traffic without affecting user experience.
What is Shadow Deployment?
Shadow deployment enables safe production testing by observing how new agent versions perform with real-world data and traffic patterns without risking user impact. The shadow agent processes actual requests in parallel with the production agent, but only the production agent's outputs are returned to users. Teams compare results to identify regressions, improvements, or unexpected behaviors before full deployment.
Shadow deployment provides invaluable testing that lab environments cannot replicate, exposing agents to real data distributions, edge cases, and load patterns. It enables data-driven deployment decisions based on actual production behavior rather than synthetic testing, significantly reducing deployment risk.
Example
Before fully deploying an updated recommendation agent, the team shadow deploys it for one week. The shadow agent processes all production traffic alongside the current agent. Analysis reveals the new version improves relevance by 8% but has 15% higher latency, informing optimization work before full deployment.
How Signet addresses this
Signet values shadow deployment practices as evidence of operational maturity in the Stability dimension. Agents whose operators use shadow deployment demonstrate commitment to quality and risk management, building stronger trust through careful change management.
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