Merge Gateway Evals De-Risks Model Swaps
Merge Gateway Evals grades candidate models against an agent’s real task suite, then shadows them on sampled live traffic before customers see the results. Teams can compare quality, cost, tokens, latency, and paired responses before changing production routing.
This is the practical missing layer in model switching: offline evals establish a baseline, while shadow traffic reveals how candidates behave against messy production workloads.
- –Uses the same Gateway path, credentials, restrictions, and guardrails as production
- –Per-session shadowing preserves multi-turn context for realistic agent comparisons
- –Cost and latency visibility turns model migration into an operational decision, not a benchmark debate
- –Confidence intervals help expose when a “better” score is based on too few test cases
- –The main risk is still evaluation quality: weak task suites can make a carefully measured migration confidently wrong
DISCOVERED
1h ago
2026-10-06
PUBLISHED
1h ago
2026-10-06
RELEVANCE
AUTHOR
merge_api