Principle
Test the agent under conditions that resemble the real job
A perfect lab result can still be a poor rehearsal.
When it fits
- An AI workflow performs well in a clean sandbox but will face different data, latency, permissions or users in production.
When to avoid it
- Similarity to deployment is contextual; do not claim full production equivalence from a test environment.
Why it matters
List the conditions that materially shape the deployed task—data distribution, tool availability, permission boundaries, response time, interaction length and failure handling. Reproduce the important ones in evaluation rather than testing only the easiest environment. Record differences you cannot reproduce.
An example
Test a support agent with realistic long threads, unavailable tools and restricted user permissions instead of only single-turn happy paths.
Check your result
The evaluation report names the production conditions represented and the material gaps that remain.
Keep this limit in mind
- Similarity to deployment is contextual; do not claim full production equivalence from a test environment.
Connected ideas
Use beforeMonitor the agent after deployment
Evidence and sources
Supports
NIST AI RMF recommends measuring AI performance or assurance criteria under conditions similar to deployment settings.
No test environment perfectly reproduces production, so material differences should be documented.
AI RMF Core · Measure 2.3