level: research
researchers released harbor adapters, a unified evaluation setup for agentic benchmarks. it ports more than 80 benchmarks so any agent can be tested across them. the work includes code review and parity checks to confirm the adapters work correctly. they also ran eight models on 54 benchmarks using terminus-2 and three native harnesses. this gives a wider view of agent skills and common failures than before.
the team built harbor-index, a curated set of 82 hard and varied tasks from 29 benchmarks. they filtered by difficulty, then used ai and human audits to remove flawed items. an audit-and-fix step improved task quality. the evaluation covered models from different capability tiers, but the paper does not list exact accuracy numbers in the abstract. the main limit is that only eight models were tested, so results may not generalize to all agents.
this work matters because agentic benchmarks are often hard to set up and compare. a shared adapter layer saves time and reduces errors when testing new models. the curated index gives researchers a smaller, high-quality set of tasks instead of running hundreds of noisy ones. it also makes failure analysis more consistent across different agent frameworks. the code and index are available for others to use.
why it matters: it gives ai researchers a standard way to test agents across many benchmarks without building custom integrations for each one.