AI · 2h ago
AI Agent Evals Miss Real-World Reliability, Developer Argues
A developer argues that most AI agent evaluations test isolated capability, not real-world reliability. Standard benchmarks use clean prompts and single turns, while production agents face messy contexts, tool failures, and ambiguous instructions. The author calls for evals that measure graceful degradation, such as retry counts and clarification requests.
Meridian48 take
The critique is valid but not new; the gap between lab and production evals is well-known, yet few practical solutions exist beyond custom failure-replay pipelines.
ai-evaluationagent-reliability