When you ship an AI agent , the key question is whether it can execute a chain of work across dozens of sequential tool calls against a live environment, and recover when a step fails. Scoring whether the model sounds right tells you almost nothing about whether the work finished .

That gap is why agent evaluation has had to evolve from scoring a single function call to scoring an entire task, with tool calling as the connective tissue underneath. This post traces that arc and explains why nearly every serious agent benchmark now rests on tool use.

Why isn’t standard LLM benchmarking enough?

The original harnesses were built for static tasks. The first model-agnostic, open-source harness decoupled the model from the evaluation protocol.

Agents broke this assumption. Operating across multi-step tasks, an agent calls tools, handles errors, and observes results over many steps, making a single output string insufficient.…