TLDR Tech / Practical
This post "covers why evaluation matters well beyond frontier labs, how Harbor standardized the e...
This article examines how scaling agent evaluations requires shifting from simple string comparisons to robust infrastructure that isolates and grades agent actions within reproducible container environments. The author reviews recurring failures in agent benchmarks—such as task drift, CPU topology constraints, and sandbox escapes via DNS or proxies—and introduces the Harbor Framework and its GKE extension, harbor-gke-ext, for managing high-concurrency evaluation and reinforcement learning rollouts.
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