Design benchmark suites that capture tool-use reliability, multilingual stability, and planning depth for frontier agent systems.
Classical language model benchmarks emphasize static prompt-response accuracy, but agentic systems operate across multi-step workflows, external tools, and dynamic contexts. Evaluating them requires measuring whether plans succeed end-to-end, tools execute correctly, and outcomes remain consistent across languages, modalities, and deployment environments. Without richer benchmarks, teams either over-trust flashy demos or under-invest in capabilities that quietly improve reliability.
Frontier teams now publish composite benchmark batteries that stress test real-world orchestration. This lesson explains how to construct similar evaluations for your own agents, especially when working with open-weight or locally hosted models that you control.