Production Evals For Agentic AI Systems
summary
Benchmarks score models 90% then 92% but production shows failures when plans call tools or long workflows run for hours. The claim is that continuous evaluation pipelines integrated into production catch drift from weekly model releases and API outages. The built system records agent traces of planning, tool calls and memory access and feeds a control plane that measures task completion, escalation rate, latency and recovery so teams see what actually works.
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