Beyond Model Benchmarks: Why Agent Evaluation Matters

Traditionally, AI benchmarks evaluate models in isolation, but two agents built on the same model can demonstrate vastly different reliability in practice. Legit fills this gap by assessing agents holistically—accounting for architecture, decision-making, and real-world performance—rather than just the underlying model's capabilities. As AI agents become critical in production, this shift from model-centric to agent-centric evaluation represents a fundamental change in how we measure what actually works.