Shadow-Mode Testing for AI Agents

Compare agent proposals with accepted outcomes before activation while accounting for data access, side effects, and residual risk.

What Shadow Mode Should Mean

A shadow workflow processes representative input and records proposals without the consequential writes being evaluated. Verify the implementation: reading sensitive production data or calling a provider can still create risk or cost.

Comparison

Measure agreement, severity-weighted errors, unknowns, reviewer effort, latency, cost, provider failures, and drift. Use representative edge cases rather than only clean examples.

Promotion

Promotion criteria should be risk-based and approved by the workflow owner. Passing a sample does not guarantee future behavior; keep monitoring, narrow scopes, kill switches, and rollback.

Frequently asked questions

Is shadow mode zero risk?

No. Data exposure, provider calls, flawed comparisons, and operational mistakes remain possible. Configure isolation and non-execution explicitly.

What sample size is enough?

It depends on risk, variability, rare cases, and statistical goals. Document the rationale rather than using a universal number.

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