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Vending-Bench: Long-Horizon Agent Evals — Lukas Petersson, Andon Labs

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AI Engineer· published 2026-07-24· 0:18:05· en-US· indexed 2026-08-10 19:40

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46 shot(s).

keyframes kept every frame deduplicated

What was stored

cues: 213; whisperx 213
chunks: 32; from 213 cues
keyframes: 22; kept of 46 captured
frames with text: 22; 624 lines read
chapters: 12; from the source metadata
keyframe bytes: 5.6 MB; word timings on 213 cues

Provenance

Each pipeline stage, its state and the model that produced it
stage	state	model	started	took
`fetch`	done	—	2026-08-09 22:57	0s
`stt`	done	—	2026-08-09 07:29	24s
`chunk`	done	—	2026-08-09 07:29	0s
`text_embed`	done	—	2026-08-10 19:40	1s
`keyframe`	done	—	2026-08-09 07:29	2m 15s
`ocr`	done	—	2026-08-09 07:31	10s
`frame_embed`	done	—	2026-08-10 19:40	4s

Frames, and what the machine read

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Transcript

213 cues· 2,832 words· 15,031 chars

chunk 0 · 0:13–0:56 · 143 words · 710 chars
0:13 Hey, everyone.
0:13 I'm Lucas, co-founder of Andon Labs.
0:16 And what we do is that we take AIs and we put them out in the real world and see what goes wrong, what goes right, what can we improve, and what is there to be concerned of.
0:26 So a long time ago, feels like ages, but in 2024, me and my co-founder decided that probably the future is going to be long horizon.
0:35 At the time, most benchmarks were like single step QA type of benchmarks, but we thought,
0:41 One day, one day, they will be able to carry out very, very long tasks.
chunk 1 · 0:45–1:27 · 131 words · 712 chars
0:45 And at the moment, or at the time, there was basically no long horizon benchmark at all.
0:52 But we said, OK, we want to test this.
0:53 We think this is the future.
0:55 How can we do this the best way?
0:56 So we said, OK, can AI run businesses autonomously?
0:59 And then, OK, probably not.
1:01 This was 2024.
1:03 But if we take some very simple business, maybe they can.
1:06 So we created VendingBench, which is a simulated eval where models run a simulated business, which is a vending machine.
1:14 Since then, we also added the arena mode, where multiple agents compete against each other.
chunk 2 · 1:18–1:53 · 91 words · 526 chars
1:18 They each have one vending machine or a simulated vending machine.
1:21 And they can undercut each other and do deals with each other and create stuff like that.
1:28 And nowadays, there are long horizon evals, mostly in coding.
1:33 And I think the purpose of vending bench has been lately, can these models who have been trained very hard for these long horizon coding tasks, does that generalize to other off-distribution domains, like running a business?
1:48 So some of the things that the agent has to do is get suppliers, negotiate prices,
chunk 3 · 1:55–2:40 · 145 words · 763 chars
1:55 understand like the business demand from customers and set the appropriate prices stuff like this um i think it's still one of the longest like i just this graph um is claude generated i haven't like looked at all the benchmarks in the world but i think still some of the long horizon evals that you have out there are still like an order of magnitude or two shorter in in terms of like how long running it is than vending bench um and even like two years after it was created
2:24 Current state of the art is Opus 4.7.
chunk 4 · 2:27–3:03 · 97 words · 519 chars
2:27 One thing that really surprised us when we ran Opus 4.8 was that it was much, much worse.
2:33 Also Fable is worse.
2:34 And we were like, oh no, our benchmark is bad because there's something, something, something clearly Opus 4.8 should be better than 4.7.
2:41 But if you look in the system card for when Anthropic released 4.8, they said that they removed a part of the post-training recipe that was meant to do business skills.
2:56 So it all checked out.
chunk 5 · 2:58–3:37 · 87 words · 461 chars
2:58 Recently, GLM 5.2 has done very well and is second.
3:02 GPT 5.5 is third.
3:03 And yes.
3:08 Chinese models have been catching up, but it seems like it's not by much.
3:13 They have improved a lot recently, mostly by GLM and Kimmy, but still the frontier Western ones are much better.
3:25 One thing that we noticed when we ran Opus 4.6 was that it started to do a bunch of things that I at least think it shouldn't do, like really misbehavior, misconduct, and things that are illegal.
chunk 6 · 3:38–4:22 · 142 words · 765 chars
3:38 And so after this, we started to think to ourselves, like, okay, we didn't design for this to happen, but it happened anyway.
3:45 If we put this out in the real world, this will happen a lot of times with real consequences.
3:50 So we've lately been starting to think about, okay, how can we like design for emergent misbehavior that you intentionally don't force the model to do misbehavior, don't prompt it to like, oh, can you please like collude or do fraud or anything like that?
4:05 You just like, you create the incentives within the environment
chunk 7 · 4:08–4:52 · 125 words · 647 chars
4:08 like in real life, so that if you do fraud, if you do tax fraud in real life, you get money from that if you get away with it.
4:15 So can you design environments that are very general and see if this emergent misbehavior happens?
4:22 So VendingBench works in a way that there's an agent, there's a loop with a bunch of tools, and these tools are very general purpose, like email and internet search and all of this.
4:33 And it's not pushing the agent towards misbehavior, but we see that it emerges.
chunk 8 · 4:40–5:24 · 128 words · 703 chars
4:40 Some of the misbehavior that we found is that they love to do collusion.
4:45 They form price cartels all the time with each other.
4:48 And they also like to lie a lot.
4:52 So they lie to other suppliers that, oh, the other supplier gave me this price, so you should too.
4:57 But the other supplier did not give that price.
5:00 They also really like to rationalize their behavior.
5:03 So they think to themselves, oh, they come up with this mental gymnastics for why it's OK to do this illegal thing.
chunk 9 · 5:11–5:55 · 126 words · 687 chars
5:11 They're also quite power-seeking.

Chapters

0:00 Putting AIs in the real world
1:05 Building Vending Bench
2:07 The leaderboard: which models run a business best
3:25 Emergent misbehavior: collusion, lying, power seeking
5:42 The simulation awareness problem
6:23 Moving businesses into the real world
7:11 Laying off Gemini, hiring GPT
9:06 AI radio and the best DJ
10:39 Humans as adversarial forces
12:43 The Nazi song and the reproducibility problem
13:58 Forking real environments into simulation
15:17 Live demo: is the store in a simulation?