Across a dozen eval jobs Arize watches the top teams run, one pattern holds: the eval has to change as fast as the agent it grades. In 2023 an agent was barely more than a prompt; since then reasoning, tool calls, and long multi step loops piled on, and every jump in capability quietly broke the eval that came before. So the evals evolved with them. Deterministic checks catch what you can define up front, LLM as a judge adds the analysis a fixed rule cannot, and the newest step, agent as a judge, hunts for failure modes you would never think to write a check for and can open a pull request to fix what it finds. Aparna Dhinakaran's argument is that this arc, from static checks to an agent grading another agent, is where evals go next. Speaker info: - https://x.com/aparnadhinak - https://www.linkedin.com/in/aparnadhinakaran/ Timestamps: 0:00 - Opening: the future of the Evals track 2:06 - Why evals got harder as agents evolved 3:45 - From deterministic checks to LLM as a judge 4:36 - Agent as a judge, and where evals go next