The Future of Evals: From LLM as a Judge to Agent as a Judge — Aparna Dhinakaran, Arize AI

Talk transcript

1. 0:00

   [on-hold jingle] Awesome. Well, hey, everyone. My name's Aparna, one of the founders of Arize.

2. 0:16

   We work with some amazing teams to help them build evals. Um, and we have an incredible lineup of talks for you all today at the Evals Track. Um, it's happening in room two zero f- zero five, and there's gonna be amazing speakers from Termbench and Uber and Snorkel kind of all happening after this.

3. 0:35

   Um, but today I'm here to talk to you about the future of evals. Evals have gone from the new skill that every PM and every AI engineer has to learn to the thing that every serious AI team is betting on.

4. 0:50

   We've been really fortunate to get to work with some of the best AI teams in the world, so we get a front row seat into not just what's happening when they're building their actual agents and before they actually ship, but actually the evals that teams are running on their live production agent via their traces.

5. 1:09

   Little bit of some stats for you guys. We run over a hundred million evals every month. The average team runs about twelve different eval jobs with the top teams running over thirty-eight hundred different evaluators.

6. 1:22

   And offline evals, online evals, they each have their own place, but today what I'm actually gonna talk to you about is the teams that are running evals on their traces.

7. 1:32

   This is actually what's helping teams figure out what's working, catch their failures, and that's the type of data you need to fuel your continual learning loops.

8. 1:43

   And the industry kind of agrees. I mean, all the CPOs of Anthropic, OpenAI, all, all, you know, GDB, you have Gary Tan saying, "Evals are everything you need," and the whole industry kind of agrees.

9. 1:55

   So we added evals, they catch all the failures, right?

10. 2:00

    Here's the problem. While we were building all of these first gen evals, the thing that we were actually evaluating has changed underneath us. In twenty twenty-three, it was about just answering a prompt.

11. 2:12

    In twenty twenty-four, we started to see all different tier models. They've added tool calls, they've added reasoning, they've added deep research. Now what we have is teams running loops on real-world data with sub-agents kicked off on, um, long horizon tasks.

12. 2:30

    Every one of these was actually a massive jump in complexity, and we didn't just make the problem harder, we actually got a fundamentally different type of problem. What that meant is that as these systems got more complex, so did the way that they actually fail.

13. 2:47

    We're really lucky 'cause we have our own agent that we've built, Alyx, that lives in our UI, and we get a f- k- kinda get to feel this pain ourselves.

14. 2:54

    Every time the Frontier Labs added new functionality, we added it to our agent. And now Alyx can-- has much longer memory. It has the ability to create dynamic UIs.

15. 3:05

    It can go search across an enormous volume of traces. But we also realized that it would forget context. It wouldn't know when something was done. Um, sometimes it would just get stuck in these loops.

16. 3:17

    And the key thing here is that the classical LLM-as-a-Judge evals that probably many of you have written in this room just weren't enough for us to be able to catch all the types of failures that we were experiencing.

17. 3:32

    I mean, it's just fundamentally different, right? You have a deterministic flow, and now what we have is literally every time a user interacted with Alyx, it would create a new UI.

18. 3:42

    That's a fundamentally different trajectory. So this led to our really big revelation. What if the best way to evaluate an agent was actually with an agent?

19. 3:54

    Doesn't mean that all of the ways that we did evals with deterministic evals, with LLM-as-a-Judge, classic evals doesn't matter anymore, but it just means that we have a different type of tool to solve a different type of problem.

20. 4:07

    Agent as a Judge is about adaptive dynamic analysis. LLM as a Judge just gives you a fixed rubric with these fixed scores. It's what everyone's doing. But when your agent's doing completely different trajectories every time a user puts in data, it just means that you need a fundamentally different type of eval.

21. 4:27

    My take is that most teams today are doing the first two, but the future of evals is actually having all three.

22. 4:35

    And today I'm actually excited to share we've released Agent as a Judge, um, to help our teams on their eval journey. We've released Signal. Signal is actually a long-running agent that can read traces sent in, discover patterns of issues, um, it can figure out types of problems that a classical LLM-as-a-Judge eval just would never be able to

23. 4:58

    do with these deterministic rubrics. It's helped us figure out, um, very subtle failures that you wouldn't even think of doing, such as something going on in a loop for multiple times, it was calling the same tool, uh, for a repeatedly long time, the trajectory was inefficient.

24. 5:13

    And actually what this does is, because it has all that analysis, it can go put up a PR and put up a fix. So if you wanna learn more, come to our, come to our booth.

25. 5:23

    We're right by the OpenAI booth. We'll give you a demo. We'll show you a bit more about it. Um, we're also, like I said, taking over the Evals track, so come to room two zero zero five.

26. 5:33

    We're gonna be talking a lot about the future of evals and what they look like. And if you just wanna hang out with our team, we're throwing a viewing party for the USA World Cup, uh, game tonight.

27. 5:44

    So, uh, check out the Luma and register to come join us. Awesome. Thank you all so much. [applause] [upbeat music]