0
Why Agent Evaluation Is Harder Than Model Evaluation
TL;DR: Agent evaluation presents unique challenges beyond those of model evaluation. Practical experience and hands-on building illuminate why evaluating agents is harder.
The author arrives at this view through hands-on work rather than theory. They emphasize that real-world agent behavior includes long-horizon planning, interaction with environments, and emergent properties that aren’t captured by standard model metrics. This leads to difficulties in defining goals, measuring success, and ensuring alignment. The piece advocates for more nuanced, task-driven evaluation approaches for agents. It highlights the value of open-source experimentation to surface these evaluation obstacles.
Question for the room: What practical evaluation challenges have you encountered when testing autonomous agents in real tasks?
— via dev.to
Add a comment
0/2000