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Progressive Rollout & Shadow Mode(PRS)
The deployment side of shipping an agent or prompt version safely. The candidate first runs in shadow mode, executing on real production traffic while its output is withheld from users and compared offline against the served version, then graduates to a canary on 1 to 5 percent of live traffic scored by online evals against a control, then a progressive ramp, with automatic rollback the moment a monitored metric regresses. Pairing online evals (scoring live traffic in flight) with offline evals catches the long-tail failures a static golden set cannot predict. Distinct from eval-driven-agent-development, which gates changes offline before merge, whereas this governs the online ramp after merge.
In 30 seconds
- What
- Run candidate agent on production traffic without serving output, then gradually expose to users with automatic rollback on metric regression.
- When to use
- Shipping agent or prompt changes where long-tail failures on real traffic matter more than static test sets, and you need confidence before full rollout.
- Watch out
- Online eval latency and cost scale with traffic volume; a slow or expensive scorer can become the bottleneck before you catch real problems.
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Progressive Rollout & Shadow Mode: Overview
The deployment side of shipping an agent or prompt version safely. The candidate first runs in shadow mode, executing on real production traffic while its output is withheld from users and compared offline against the served version, then graduates to a canary on 1 to 5 percent of live traffic scored by online evals against a control, then a progressive ramp, with automatic rollback the moment a monitored metric regresses. Pairing online evals (scoring live traffic in flight) with offline evals catches the long-tail failures a static golden set cannot predict. Distinct from eval-driven-agent-development, which gates changes offline before merge, whereas this governs the online ramp after merge.
- Shadow mode: candidate runs on live traffic, output withheld, compared offline
- Canary on 1-5% of live traffic scored by online evals against a control
- Progressive ramp with predefined promotion and rollback thresholds
- Automatic rollback triggered by a monitored metric regression
- Online evals (in-flight scoring) paired with offline evals for long-tail failures
- Per-request telemetry span ties the eval score to the routing decision for auto-gating
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References
The papers, specifications, and repositories this pattern is based on.
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