GPU Sandboxes and Agent Eval Environments

Different tools work at different stages of agent development, not just one platform for all.

Published benchmarks overstate agent capability by ignoring planning failures and tool-use realism.

Matching evaluation frameworks to agent architectures prevents costly deployment failures.

A benchmark's design choices shape what its scores actually reveal about model reasoning.

Benchmarks built for chatbots don't measure what infrastructure agents actually fail at.

Multiple trials reveal whether AI agents work reliably, not single test runs.

Focus on what completion rate actually measures to avoid shipping unreliable agents.