Standardized Protocols Needed to Make AI Agent Benchmarks Reliable

AI coding-agent benchmark scores are often misleading because they omit critical context such as prompt text, tool allowlists, sandbox configurations, and grader settings that can influence results more than the model itself. Researchers argue that a registered protocol — documenting corpus identifiers, holdout sampling rules, decoding settings, and network policies — must accompany any published pass rate to make scores comparable. Negative-control canaries, tasks with known expected outcomes, are proposed as a built-in harness check to detect grader leaks or environment drift that could silently corrupt results. Published benchmark runs should also include attempt counts, wall-clock time, token costs, and variance across seeds, since two agents passing the same tests at vastly different costs are not equivalent. Without these standardized requirements, leaderboard percentages function more as marketing screenshots than as reproducible scientific measurements.