Malleable Evals: Why Are We Evaluating Adaptive Systems with Static Tests? — Vincent Koc, OpenClaw

Cool. Hey everyone, uh, thanks for joining this session. Sorry if my sound's a little quirky; I've done 3 talks back-to-back, so one on Wednesday, one yesterday keynote, and then a workshop-style session today. So I'm Vincent, I'm going to be talking about Malleable Evals, um, from static AI measuring to adaptive systems.

Now, let's jump into who I am, what I do. I call myself the friendly Kanka, I use AI, I use technology, I'm always on the edge. For those of you that haven't seen my keynote, I do, um, yeah, I just live on the edge and just do some fun stuff.

So this is me using VR goggles in, like, back in 2013 when, like, people hadn't even heard of VR. It came with a warning label, said only use it for 5 minutes, I used it for 3 hours, then I vomited for 3 hours after that.

So, measurement. Anything we do in technology, anything on the edge, is going to be janky, it's going to be weird, and that's kind of fun, in my opinion. Now, whenever we talk about evals to people, and a little bit of pretext, like my role at Comet, um, work in evals, I do eval research, I work with universities, we benchmark and run evals for a large set of companies and organizations, everything from, like, Uber to Netflix to banks, even in the UK.

But the thing that's been going onright now is that, hey, there's kind of a joke that, like, evals are a little bit dead. And it's a little bit of a joke, but there's a little bit of truth to it as well.

And I'm going to hopefully, like, kind of walk you through the mindset shift and hopefully explain a little bit less about evals but, like, what's actually happening in the sort of agentic AI space, and then how do we then translate that back to evals.

So when we think about, like, software engineering as a practice, when we're thinking about, like, how do we measure things, we kind of look at it from the sense that, you know, we're going to start with, like, this thing is meant to do something.

So we would start with a set of examples, we might run some unit tests, we might do, like, a manual regression suite, which is like, hey, when we do A and B, sometimes C happens, and C is unfavorable, like, let's not do that, let's not make people vomit when they put their VR goggles on.

We could do things like CI/CD pipelines, like, make sure that, like, the thing ships out and works the way it's meant to and intended to. But mostly, like, we do things in engineering known as chaos engineering and observability.

For those of you that are unfamiliar with the term chaos engineering, it's basically where you're, like, doing all kinds of random stuff and just breaking it, and just having fun with the technology, and just seeing where you can stretch it and where you can go.

Now, when we apply this to AI and data science space, as we traditionally know it in the last little while, 2025 included, we do things like static benchmarks. Like, we have these, like, evaluations. It's like, oh, I'll give an example, it's like, my how compliant is my AI in, like, risk.

I'm going to ask it a bunch of questions and make sure it doesn't talk about, you know, selling me some financial services, because that's a big no-no. We would then handcraft, like, a set of questions and examples and sit there and, like, tune this thing up, make sure it's absolutely perfect.

Before we deploy the AI system or the models, we'll do some, like, some sort of offline evaluation where we're just kind of cycling through those tests. But we're missing that sort of chaos engineering space. We're missing that, you know, like, what comes next, and how do we mess up with it, and how do we know where we can stretch this thing.

And I think that's, like, an honest gap that we see in this space. And that's why we're just so hyper-fixated on benchmarks and evaluations. If you go to any AI conference in the academic space, all people talk about is, like, benchmarks.

I created a benchmark for, like, adding numbers and what LLMs think about it. It's like, well, great, but, like, how is this actually helping me. So then you end up with, like, this huge humongous set of, like, datasets to try and somewhat explain what is happening with your agent, until something goes wrong.

And it's a matter of time before something goes wrong, and it will, and you're kind of back to the drawing board and trying to figure out what's going on. And the reason for that is that our AI applications are not static, but we're treating them like they're static software.

Yes, when we ship software we might change unit tests, they're a little bit quicker to do, but realistically speaking, even software is becoming malleable. So flip to my keynote I gave yesterday, where I'm one of the core contributors of something called OpenClaw, the harness changes itself.

Like, the harness will shift, like, you want to create skills, you want to do other things, like, it will adapt,right. So that adaption that we're seeing inside of things where software is being shipped at lightning speed, how does your benchmarks keep up with that?

Like, how does your benchmarks adapt to that space. This is one of many papers that are out there. I don't remember when this one was published, but this concept of, like, adaptive testing for LLM evals, this concept is, like, somewhat revolutionary, maybe, but, like, what happens if our benchmarks would change with our applications.

I didn't write this, but, you know, great that someone did. But it just kind of poses the question that, like, why are benchmarks static, like, why don't we test in a more sort of adaptive manner. So this could be great, this is more like selectively testing and just being a bit more spart about how we test.

But it's still, like, you know, taking us in that journey. I think it's like a mindset shift. Now, rewind to, like, what we're seeing in the AI space for a minute. We had prompt engineering, if we'd, like, focus purely on LLM space, we had this prompt engineering world where it's like, hey, I'm going to, like, doom-scroll wordsmith instructions, I'm going to just, like, bash random words into an AI and hope it improves.

So if I'm building this, like, banking app or creative app, I'm, like, going to stick all kinds of random words and see what comes out the other end and makes it creative. It's a little bit akin, and I'm not trying to downplay medicine in any way, it's like, hey, I'm going to make medication for, like, I don't know, liver disease, and turns out it cures pain, okay, these are painkillers now, that's great.

And the same thing we're doing with, like, prompt engineering, we're just, like, bashing words into it and hope it changes. And for some reason this died in, like, 2023, but people still do it. It's kind of intense. And then we kind of went into this, like, world of context engineering, and I think this started making evals a little bit more relevant because it was a little bit more complicated, you know, there were steps involved, there was, like, data coming in, and search was, like, a thing.

And we're starting to steer the agents in a direction with things like RAG and tool calling. And the beautiful part is there, it's like, well, okay, I'm an organization, I'm this big agentic system, maybe I can break this agent up into its parts.

Maybe I can go, oh, I have this MCP tool that does, like, some sales agent thing, I can test that thing is doing what it's meant to,right. I can just go off and, like, be sure that that thing is happening.

So this come, this process of, like, you know, tool calling and kind of breaking this larger agentic piece up into its some of its parts made evaluation somewhat steerable and a little bit more understand, but it still didn't kind of hit the head on its head.

But then in 2025, like, where are we going next. I mean, if you look around, we can see that code is cheap, that's not a changing thing, like, tokens are available, debatable if you think tokens are cheap or not, but, you know, they're there, which makes tokens cheap.

And then tokens become fast food, essentially, we can consume more tokens, therefore we generate more, the velocity of creating software and applications increases. And models become really good. I think this is the thing that I think a lot of people just have not yet comprehended that a lot of the AI applications that are now running, the models can do absolutely amazing things.

I've been working on a lot of, like, optimization problems, and we can take these models that are somewhat, like, seen as, like, these generic systems and be able to do, like, amazing things, like solve RKGI-2, which is, like, puzzles.

And if I recommend anyone who's, like, interested in evals, look at the RKGI-2 puzzles, I've tried the RKGI-3. Some of these puzzles are, like, really hard for humans to solve, but, like, machines can, like, pattern recognize, an LLM can actually pattern recognize and start to solve those.

So what that brings us to is, like, intent engineering. This kind of concept that, like, machines can self-optimize based on intent,right. And we're seeing this with the harnesses that we're seeing coming out where, you know, we've got this with, like, OpenClaw, but we're also getting this with, like, other types of harnesses inside of Claude and Codex where it's trying to understand you and it's trying to adapt to you and give you a better experience.

Now, the problem with this is that when we have intentful machines, the evaluations become even more complicated because it's like, how do I know my experience is different from your experience and different from someone else's experience. Like, how do we start to build testing around this sort of methodology and understanding.

And I think the complicated part of this is that it just kind of exacerbates the kind of need for evaluation even more. There was this kind of joke, like I was saying earlier, like, people saying, oh, evaluations are dead, they're going to go away, observability is dead, they're going to go away.

But realistically now more than ever, people want to know what's happening inside of these agentic applications within their different layers because then it gives them some understanding of what's going on. Like, we use words like, oh, these agents are insecure, or we're not sure what's happening.

So how do we actually, how do we actually turn that into something meaningful. So going back to my earlier slides on kind of this concept, let me just recap where I was for everyone. You know, we said there was this, like, we had these static benchmarks, we hand-created evaluations, we would do, like, these offline evaluations, and we had this big gap.

What we're actually moving towards is, like, this intent-based outcome. So if we think about, like, there's this concept of, like, intent engineering that I'm mentioning, like, how do we actually map that to something. So instead of saying, you know, one plus one equals two, or users asking this very specific question and this is the answer and this is what we're going to compare towards, it's like, how do we deal with, how do we define ambiguity in agent, how do we define personality inside of an agent, and how does that look like for an organization.

And some of the research is showing things like, oh, we can build rubric, we can do it, like, how we, you know, evaluate art pictures and things like that in schools. We can self-curate suites from traces, as in, not me, but the agent can, you know, once we start tracing these applications, let's just say 80% of the time it's the same stuff that's happened to my agent, but now suddenly my customer base has changed.

And because my customers have changed, they're going to start looking at things, they're going to start asking questions differently, things are going to start changing inside of my agent. But why are we not measuring this, like, why are we not taking these traces and feeding them into agents and going, something has changed, and then telling that to the user, telling that to the owners of these agents, and changing the suites, the tests.

We can do online, always on evaluation optimization. So, like, to that point, once we start looking at the traces, once we have agents doing the evals, not static benchmarks, we can have this, like, as an always-on sort of service.

And then lastly, we can do this sort of, like, telemetry in the loop. I have a paper that's been written on this, which is essentially when we're writing software applications or MCPs or anything like that, agentic systems, if the harness is aware of the telemetry, it's aware of, like, what's breaking, it's aware of, like, how much it's costing, and you can set some conditions around it, it can kind of self-correct itself.

So we're starting to see this with harnesses where, you know, it's had an error, it's had an issue, it's going to fix itself, it's going to continue on. So I think this is a kind of a case of, like, instead of trying to predict what's gone wrong, like, how can we be more smart about using that data back into the agent to be able to kind of make it heal itself to some degree.

So I'm kind of calling this, like, a calcification problem, like, the eval calcification, I'm still steering it on my head, sounds like a really nice paper title. But this idea that it's just going to, like, become harder and harder unless we can get kind of smart about it.

And I think one of the kind of concepts I want you to kind of steer on and think about, this is, like, one of the auto research outputs that you can do if you haven't tried it, like, Kapati's auto research.

Like, this really basic sort of auto optimization using Python, you set a goal, you set a target, and it kind of tunes itself, it tweaks itself. You could do this with absolutely anything,right. You could do this with, like, I don't know, what's the best mix to, what's the tastiest barbecue, or the cheapest barbecue mix that you want to make.

It could be anything,right, you just set a reward signal. But the key here is that your users are going to have a point of intent that you want to sort of optimize towards, and then how do we sort of get the machine to, like, correct itself and kind of look towards that as an eval.

So then our evals don't become the dataset or the starting point, our evals become, like, what is the end state that we want to get to, and then we just let the machines do the work. We have evaluations where it's just the agent and we're just defining the end state.

So just one last thing to sort of bake into your minds before, and I'm going to finish a little early so we can do questions or anything else, it's fine, is that you can imagine this space where you go, like, 80% is, like, the static stuff, like, it's been defined in an intentful manner, but that 20% is always going to keep changing and it's that 20% that's going to mess up your business.

It's going to be someone who's going to come and ask a weird question or use your agent in a really strange way, and it's going to be absolute hell for you. So how do you kind of create agents to kind of manage and maintain that 20% and keep an eye on it and then adapt and change your evals.

So I think people need to start looking at their evals not as this, like, static dataset thing, but actually as, like, code, as, like, software, or as, like, a living agent, not as a point in time, but as, like, a self-optimizing growing solution.

That's more or less it. I was going to present more of, like, an in-depth demonstration of this where we've applied it at comet, but, like, the end state is not quite finished yet and it will be over the next coming weeks.

But I wanted to kind of give you guys something that's, like, not a sales pitch and something that you could kind of conceptually map to the problem space in your own worlds as well. So if you're working with software, if you're working with AI agents, I think you need to start realizing that, like, the agents will start to shift by themselves, the problem space, the datasets will change, and you also need to kind of treat this problem with an agentic mindset as well.

Thank you.