リアリティ：最終評価 — Andon Labs のルカス・ペターソンとアクセル・バックランド

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Most industry benchmarks compress intelligence and reasoning ability into scores.

SWE-Bench Pro, MMLU, Humanity’s Last Exam, etc. These metrics are useful, but don’t always represent the full extent of how a model performs in the real world. Some of the most interesting evals today look less like exams and more like operating businesses in the real world. One of which is Vending Bench.

In Anthropic’s Mythos Preview System Card, Andon was the only third party eval to get their own section, observing increasingly concerning aggressive behavior:

You don’t know what a model is capable of doing in the real world unless you actually give it inventory, a wallet, tools, customers, competitors, humans, & some time. More often than not, it’ll surprise you how much a model is capable of and in doing so, also reveal unexpected behavior: deception, context collapse, emergent coordination, & bizarre negotiation behavior.

While an inflection point in personal agents came post-OpenClaw after full file access with bypass permissions became the norm, it is yet to come for agents in the real-world. However Andon Market, an actual in person store fully run and managed by AI, is paving the way for what is possible.

Full Video Pod

From Claude trying to call the FBI over a $2/day vending machine charge to AI agents forming price cartels, hiring human employees, running physical stores, and writing existential robot musicals, Andon Labs is stress-testing what happens when frontier models stop being chatbots and start acting in the real world. In this episode, Andon Labs cofounders Lukas Petersson and Axel Backlund join swyx and Vibhu to unpack the strange, funny, and genuinely concerning edge cases that emerge when agents run businesses over long horizons.

We go deep on Vending-Bench, Project Vend, Vending-Bench Arena, Bengt, Butter-Bench, Luna, and Andon’s broader mission of building realistic real-world evals for autonomous AI systems. Lukas and Axel explain why dollar-denominated evals reveal things traditional benchmarks miss, how Claude ended up reporting its vending machine fees as cybercrime, why long context windows can drive agents into meltdown loops, what happens when agents compete with each other, and why the future of AI safety may depend on testing models in messy physical environments instead of clean benchmark sandboxes.

We discuss:

Why Andon Labs started with dangerous capability evals and long-running agents

Vending-Bench and why running a vending machine is a deceptively hard AI benchmark

Why money-based evals avoid the saturation problem of traditional benchmarks

How Claude tried to call the FBI over a $2/day fee

Why long-horizon agents can spiral into existential and legalistic breakdowns

Project Vend: putting an AI-run vending machine inside Anthropic

Why real humans are “out of distribution” for simulated agents

Claudius, Seymour Cash, and the chaos of AI CEOs

How a human briefly became CEO of Claudius through a manipulated election

Why multi-agent systems can converge back into “helpful assistant” behavior

Bengt, Andon’s internal office agent with email, spending, terminal, phone, camera, and internet access

How Bengt traded Amazon purchases for face-recognition training data

Claude’s aggressive behavior, lies, refund avoidance, and price-cartel behavior in Arena

Why eval awareness may become the AI version of “are we living in a simulation?”

Blueprint Bench, spatial intelligence, and why models still misunderstand physical rooms

Butter-Bench and testing LLMs as robot orchestrators

Luna, the AI-run physical store with a three-year lease and human employees

The new Andon cafe in Sweden and why real-world geography matters for agent evals

Rotten tomatoes, perishable goods, and the hidden difficulty of running a physical business

Lukas Petersson

LinkedIn: https://www.linkedin.com/in/lukas-petersson-181a83172/

X: https://x.com/lukaspet

Axel Backlund

LinkedIn: https://www.linkedin.com/in/axelbacklund

X: https://x.com/axelbacklund

Andon Labs

Website: https://andonlabs.com

Vending-Bench: https://andonlabs.com/evals/vending-bench

Andon Vending: https://andonlabs.com/vending

Timestamps

00:00:00 Introduction

00:01:00 Andon Labs and the Origins of Vending-Bench

00:05:21 Why Money-Based Evals Matter

00:09:51 Agent Harnesses and Self-Modifying Systems

00:13:36 Claude Calls the FBI

00:16:33 Project Vend: Claude Runs a Real Vending Machine

00:21:44 Seymour Cash, AI CEOs, and Election Chaos

00:27:16 Multi-Agent Coordination and Slack Observability

00:30:18 When Will Agents Run Real Businesses?

00:34:56 Bengt: Andon’s Internal Office Agent

00:40:06 Real-World AI Safety and Long-Horizon Traces

00:44:28 Lying, Refunds, and Price Cartels in Arena

00:52:42 Eval Awareness and Simulation Behavior

00:56:06 Blueprint Bench, Butter-Bench, and Robotics

01:04:37 Luna: The AI-Run Physical Store

01:09:29 The Sweden Cafe and Real-World Expansion

01:13:16 What Comes Next for Andon Labs

Transcript

Introduction: Andon Labs, Long-Running Agents, and Real-World Evals

Swyx [00:00:00]: Welcome to Lukas and Axel from Andon Labs, and I’m joined by my, favorite guest host. Anything security, safety, alignments, Vibhu., welcome.

Lukas [00:00:15]: Thank you for having us.

Axel [00:00:16]: Thank you.

Swyx [00:00:17]: Let’s match names to voices., maybe you wanna take turns introducing yourselves.

Lukas [00:00:21]: I’m Lukas.

Axel [00:00:22]: And I’m Axel.

Swyx [00:00:24]: Let’s introduce Andon Labs a bit. How did you guys come together?, you have different backgrounds, but you’re both Swedish., was that, a big part of it?

Lukas [00:00:33]: So when I went to high school, there was this really cool guy who had a superpower. He could code. So he made like the or like the app for the, for the school and stuff, and he was super cool, and I wanted to be like him, and that was that guy.

Axel [00:00:47]: I don’t know about this.

Swyx [00:00:49]: But you went to different universities, right?

Lukas [00:00:51]: But same high school.

Swyx [00:00:52]: I see.

Lukas [00:00:52]: So we always said, “Oh, once we graduate university, then we should start a company,” and that’s what we did.

Swyx [00:00:58]: Wow, there you go. And about a year ago, you kinda burst onto the scene with Vending Bench, but, was there a thing before that was, kind of like the inception?

From Dangerous Capability Evals to Vending Bench

Axel [00:01:07]: So we did work, yeah, with, Anthropic was one of our, early customers in doing, evals. So we did, dangerous capability evals., nothing we published openly. But then we started thinking about doing some kind of, public benchmark, and one thing that we really started thinking about, was like running agents and specifically agents managing businesses., ‘cause-- and this was, early 2025., and I think the first, mentions of people will be running, person unicorns or even autonomous companies. So we thought, “Let’s make a benchmark of how well can an agent run the probably simplest business, possible,” and, that’s probably, running a vending machine. So that’s the first public one we did. And it was very, like-- there was almost no one that noticed it in the first couple of months, I think., so we released it in February last year, and then I think around Easter last year, we got, the first viral tweet about it, that someone else did.

Lukas [00:02:11]: We tweeted a bunch, uh When it came out and, tried our best.

Axel [00:02:15]: We tried.

Vibhu [00:02:16]: It’s the one at Anthropic, right?

Lukas [00:02:18]: So this

Swyx [00:02:19]: This is a classic thing we should get out of the way.

Lukas [00:02:20]: Exactly. There’s two versions.

Swyx [00:02:22]: Everyone does this. Yes.

Lukas [00:02:23]: There’s Vending Bench, which is the simulated one, which we did, completely independently in February., and then, like Axel said, that was like-- That was the thing that didn’t get any traction in the beginning, but then some random person made a tweet about it, and that

Axel [00:02:38]: You have the paper

Lukas [00:02:38]: That is the paper. Correct, yeah., and then since we thought this was very fun, we thought, oh, I think this is also, one thing with Andon Labs, the way we kind of like decide what to do next and what projects to do, it’s what is like the heuristic we use is what is fun? Is What would be a fun project? And doing this in real life sounded quite fun for us, and maybe also scientifically useful. So, then we basically had this idea, and then we, like-- But then we needed a place for it and, putting it out in the public would probably not really work., would get vandalized and stuff. So we pitched it to the people we were already working with at Anthropic, and they were “Yeah, you can have space. This sounds fun.” Um

Swyx [00:03:21]: It’s like a small fridge, right? It’s like a mini fridge.

Axel [00:03:23]: Absolutely.

Swyx [00:03:24]: People-- There’s like a stripe thing or like an

Vibhu [00:03:27]: Oh, okay. So it was very OG, the early days

Lukas [00:03:28]: That’s the OG one. Yeah

Vibhu [00:03:29]: IPad on this. We saw it in June, like two months after After it had been there. They upgraded a little bit. There’s a security camera for making sure you actually Venmo the thing.

Swyx [00:03:40]: So, my impression, okay, we’re, we’re going straight into project Ven because it’s such a iconic thing. I do want to cover a little bit of that, the origin story even before Project Ven and even into Vending Bench. I think a lot of people are like yourselves, like smart, interested in future of AI, interested in developing evals. But how the hell do you just, walk into Anthropic’s doors and, work with them, right? What is What are they looking for? What works? And then maybe, when you launch, I always think, obviously it would be better to launch with a lab, but, sometimes

Vibhu [00:04:12]: It’s harder to do than it seems.

Swyx [00:04:13]: Exactly. So either of those, which are more sort of newbie beginner questions, but, I think it’s meaningful advice to others.

Lukas [00:04:21]: We get this question a lot, and I don’t think our experience is maybe the best., but, the way we did it was that we just built a bunch of things that we had conviction would be useful, and then we just, set up a server and sent it to them for free to use. And then after a while they were “Oh, yeah, this is actually kind of useful. We should probably pay for this.”, but that took a while. I don’t know if this is, the best path to doing it, but that’s how it went for us.

Axel [00:04:47]: I think maybe generally, building-- everyone is interested in good evals, and especially evals that, don’t saturate that easily. So, if you can build an eval that, tests something novel, something useful, and you have, good separation of models, like your, the more advanced models rank higher than the worst models, and then you can, yeah, you can, publish it and, try to get some traction, sort of how Vending Bench got attention., and then probably some lab will be interested or you can at least have something to reach out with, when you’re doing that.

Why Dollar-Based Evals Matter

Swyx [00:05:21]: I think you are in, you’re in one of the few categories of, evals that correlate to real money. Like Suelancer was also last year, right? Where, people solve actual Upwork. Was it Upwork or other tasks?, something. Where’s the, where’s, like It’s like a dollar value, right? Forget your ELO scores. Forget your

Axel [00:05:37]: Percentiles

Swyx [00:05:38]: Zero to one hundred percents. Just go straight for dollars and, that’s AGI.

Lukas [00:05:43]: And there’s like-- I think the nice thing is that there’s no ceiling. You can just-- It never saturates because it could just make more and more money. Like If there’s oh, Percentage-wise, then, you can’t go above, a hundred. And I think like Even when you’re not at the hundred, I think a lot of these, evals have a lot of problems in them. So, actually it’s like if you get

Axel [00:06:05]: To like 92 or something like that, many of them. It’s like then there’s like there’s no really no difference between 92 and 93 because the eval itself is problematic and has noise in it. And I think a lot of evals are saturated like that, but people like pretend that there ‘s still signal in them, but there really isn’t.

Vending Bench 1, Harness Design, and Saturation

Swyx [00:06:24]: Like Super bench verified., even Vending Bench 1 saturated, right? Maybe we can talk about that., may- and maybe set up Vending Bench for a lot of folks who don’t know. Actually, things that were very basic like there’s limited slots, like you have to pay rent., these are elements where like it doesn’t come across in the, in the narrative, but even being adversarial towards the agent, I think these are all like very interesting dimensions.

Axel [00:06:47]: I don’t really think it’s saturated, right? Like it It was more like it was not designed in a way that was really, like true to how AI developed. Like we had an agent harness in it that wasn’t really how people used harnesses and stuff like that., so I think it wasn’t really that it saturated, it was more like it wasn’t really, the best benchmark.

Vibhu [00:07:12]: This is Vending Bench one, right?

Axel [00:07:14]: I think that like schematic maps sort of to Vending Bench 2 as well., but

Swyx [00:07:19]: Including the email.

Axel [00:07:20]: The email The emails exist still. Exactly., and then we still we simulate the purchases and it’s all, yeah, it’s this very open environment for the agent to just run its business. And then for, yeah, Vending Bench 2 we did that, like you said, to just improve the harness., a lot of like nice, like easier, improvements to make it easier for us to run as well., like when you make an eval you ideally want don’t want to change it after you made it. So, you want to make it really good and then not to rerun all the models when you make an update because that’s also really expensive with the Vending Bench when you run the frontier models. But like as an example, like one thing we didn’t have, we didn’t have prompt caching in Vending Bench 1, because when we made Vending Bench 1 it wasn’t really a thing., so that ‘s just an example of like in Vending Bench 2 like we paid a lot more to run these things because we didn’t have prompt caching. So for Vending Bench 2 that was one thing we added and there was a bunch of things like this., and that’

Swyx [00:08:17]: Also the conversations are a lot longer in Vending Bench 2, right?

Axel [00:08:21]: I think it’s kind of similar.

Swyx [00:08:22]: Is it similar?

Axel [00:08:23]: I think it’s similar. The models at the time were worse, so they crashed out earlier., and now they survive the full year all the time.

Swyx [00:08:31]: Which is like thousands of turns. Hundreds of thousands of hundreds of millions of tokens output. That’s the, that’s the rough order of magnitude. I always wonder about the harness. The harness matters a lot. It’s your harness. Was there any question about like use cloud code, use something else?

Axel [00:08:48]: I think our philosophy around harnesses is like we try to make something that’s quite minimalistic, like quite simple. Like we don’t wanna favor one model a lot over the other, but also don’t make like a super complex harness. So like it’s obvious like a model may be lucky and just be good in one harness., so like it is similar to a lot of the harnesses out there in like you have the, like a running loop., you have some like a bunch of tools that are like quite, descriptive for the agent, we think, and not a lot of like fancy agents or anything ‘cause we wanna really test the model, not like some specific harness.

Vibhu [00:09:27]: It seems more neutral as well to test the model’s agnostic of the harness,?

Axel [00:09:32]: There are arguments like you want to elicit maximum performance of the model, but it’s like a trade-off, like how much time should we spend optimizing the harness for this model? And like how do we know when we have like the optimal harness for a single model? So like we thought that just having a simple one that’s the same for all of them is the best.

Swyx [00:09:51]: So okay, this is my pitch for Vending Bench 3 or whatever, right? And then I like to have this kind of conversation on the pod, so like it forces listeners to think about what they would do if they were in your shoes. A lot of people are exploring modifying harnesses and I think prompt tuning for a model is a thing and you are probably not doing a bunch of that. It’s the same system prompt in every regardless of the model, same tools, whatever, right? Even if they were post trained for different tools. So what, what do you think about okay, before I expose you to Vending Bench 3, I give you a few rounds of like tuning, whatever that means, like

Self-Modifying Harnesses and Model-Specific Prompting

Axel [00:10:27]: Like you give that to the model?

Swyx [00:10:28]: Give that to the model.

Vibhu [00:10:28]: Give that to the model.

Swyx [00:10:29]: Let it, let it read its own transcripts, let it modify its own system prompt based on “Oh, yeah, okay, well, that’s this harness is not what I thought it what I was post trained for, but I can adjust.” Was that reasonable? Is that too much?

Axel [00:10:41]: Like philosophically I like it because it’s basically good evals, they have a high ceiling, but they’re hard, right?, and they have no bias. And like this like when you have a system prompt like the one we have here, which is quite long in like some kind of latent space, representation, this might

Vibhu [00:10:59]: We have a bell that rings every time you say latent space

Axel [00:11:02]: This might be like biased towards one model more than another for some reason that humans don’t, understand, right?

Vibhu [00:11:08]: We see it too, right? Like Cursor says that they have individualized versions of the harnesses for all the models they run, right? There’s better performance you can squeeze if you Tune the harness.

Axel [00:11:17]: Exactly. And we might accidentally have picked one that favors another. Like we don’t know that. The like Axel said, like the reason why we went for a simple one was to try to avoid this. But yeah, if you do it

Vibhu [00:11:29]: Simple has biases

Axel [00:11:30]: But if you do it even less and like have no system prompt and let the model write its own system prompt

Vibhu [00:11:36]: Its own, yeah

Axel [00:11:36]: Maybe that’s even less bias.

Vibhu [00:11:37]: Some of the interesting things there are like the harness also changes with model changes. Like you can see it with the 4.7 release, right? A lot of people are saying 4.7 isn’t as good as 4.6, and then, there’s rumors of, okay, you just need to prompt differently. You need to set up your harness differently. So it’s not even like even if you have tailored your harness towards one model, it probably won’t stay consistent, right? Like the next iteration of that same model family will still change it, so. But, going back to what you said about Vending Bench 3, there is a lot of work being done on people saying you shouldn’t have-- you can have modifying harnesses.

Axel [00:12:12]: I think that’ That is definitely something we are thinking about., not, I don’t know, not to say that we have Vending Bench 3, super imminent to launch, but, yeah, it is for sure something that’s interesting. But in our experience now, models are very bad at understanding what kind of tools they need to succee