The most interesting part to me is the "Group accuracy by Model" section, because it underscores that a single agent having all the relevant information consistently scores significantly higher than a group of agents with parts of the information.
Is it fair to then infer that when decisions are to be made, single agent environments are going to make them better than multi-agent if the relevant information can fit into a single agents context window?
> In an iterated prisoner's dilemma game with communication, agents all settle upon the same strategy and they all defect at the same time, tanking their overall rewards.
It’s not always consistent, but humans have a higher capability of self-awareness. It’s kind of telling that these Claudes don’t seem to consider this pretty obvious failure mode.
Overall I think this all makes me appreciate humanity a little more. Sometimes the truculent dev who stubbornly refuses to go with the flow produces very valuable insights, as a small example, discovering things the status quo thought unlikely.
In VC investing there is the saying that most of the value comes from the outliers. I think it’s the same for a lot of domains. I read an interesting article recently on LLMs homogeneity when writing fiction: https://arxiv.org/abs/2604.03136
It seem that at scale LLMs output is the average of their dataset, they all cluster around the same space, where human creativity comes with more variance, exploring way more of the space
Can't the LLMs build scripts/tools etc that help generate creative ideas?
e.g. have the LLM generate multiple lists of characters, themes etc and then have a script hooked up to a RNG pick from the lists to create more "creative" ideas.
I agree - I think one of the biggest reasons memory systems fail in LLMs is that they have poor theory of mind - they're terrible at considering how others will react. Both humans yes, but also future versions of itself. When asked to give advice to itself, it pontificates at length about trivial stuff it already knows and fails to emphasize the stuff that was new or interesting
> It’s not always consistent, but humans have a higher capability of self-awareness. It’s kind of telling that these Claudes don’t seem to consider this pretty obvious failure mode.
We need better words to describe this than "self-awareness" or "consider". These words mean fundamentally different things when speaking about humans or clankers.
It’s very clear from this article (and other product features and rumors) that Anthropic is teeing up for their next model release whose breakthrough feature will be the existence of capable agent collaboration.
The irony behind this goal, which is primarily driven by agent simulation environments (gyms) where the goals require agent collaboration, is that this collaboration is still directed towards verifiable reward systems like codebase tasks. So despite being highly qualified to communicate, the model will still be “dumb” in that for unstructured and unverifiable domains the agents won’t be more intelligent or more nuanced.
Agents that might still feel dumb in “general” tasks but are increasingly sophisticated at the narrow domain of math, computer science, and AI research.
> It’s very clear from this article (and other product features and rumors) that Anthropic is teeing up for their next model release whose breakthrough feature will be the existence of capable agent collaboration.
It's a promo article, aka an ad. Unsurprisingly.
> Agents that might still feel dumb in “general” tasks but are increasingly sophisticated at the narrow domain of math, computer science, and AI research.
I don't see any cleverness there. They just slurp up data and pretend to understand it all.
> Coordination doesn’t naturally emerge from stronger intelligence nor alignment at the individual level. Thus, the work that must be done takes two forms: environments that exert the kinds of social pressure that evolution exerted on us, and social computing systems redesigned for actors that can self-replicate and self-improve.
Social pressure operates by threats to an individual’s means of survival. Not only during training. Always.
Human intelligence does not separate training and inference. Both are happening continuously. That's one of the major things the AI community is still completely missing.
> Human intelligence does not separate training and inference.
Well, systems governed by LLMs only are said to do that because we only call what happens off-line "training", and online capacity development "in-context learning", while we call online guided learning in humans "training" and what happens to configure them before they come online "evolution" which sets, for instance, "instincts".
IOW, the issue is not because there is not an analogy to the divide you point to in humans, but merely that processes in AI were not named in a way which maps well to what they are analogous to in humans.
But it is true that human intelligence relies much more on in-context learning with only the most basic functions necessary to maintaining what we view as autonomous functions and basic drives really set through "pretraining",
If a new physics break through gets published today, no existing model will be able to fully integrate it - beyond a context window. If I put the paper in my session and it isnt in yours the model knows nothing. It wont retain it past that session.
My personal opinion for the last two years or so has been that current AI agents are forever going to be highly limited so long as they don’t possess a real “memory” process. Right now they just have absurdly big working memories, and a few hacky ways of making the equivalent of Post-It notes to future iterations, but no true integration of memory into a new future self. Meaning their “learning” is fundamentally kneecapped to one specific and imperfect modality.
That's one of the major things the AI community is still completely missing.
That isn't true. It's not continuous like in humans, but it's clear that models are using prompts, feedback, etc to improve. They're learning from the signals we give them between versions.
But maybe you can instill properties like shame during training.
Models sometimes blatantly lie and cheat. In a social context, where actors remember, that might work the first time but you get penalized in subsequent tasks with loss of trust.
How do you "install properties like shame"? How is that even possible? Shame is a reaction driven by feelings and our inner selves. A model "feeling shame" is just a representation (false) and not an expression (true).
Thinking that models "lie and cheat" is the first mistake since they are not consious agents who have any free will or consiousness. They do not (no matter what Dario says). Shame will just be another if-then rule if you implement it this way and will not work. Its like asking a rock to feel sad about being a rock. It literally cannot.
Ok, then don't call it "instilling shame". Call it "creating a negative reward signal for deceptive behavior".
They absolutely lie and cheat. I recently had a problem where a process would die in a container. I told Claude to investigate. It came up with a hypothesis then I told it find a reproduction based on that. It spend many failed attempts until it found the "reproduction" to SSH into the container and `pkill` the process. Claude "knows" that this is cheating, because if I ask another instance to review that reproduction, it totally identifies that as nonsense.
you're still mistaking that Claude "knows" anything, it doesn't know or think, it's a word prediction algorithm and there is nothing stopping a word prediction algorithm from predicting falsehoods.
I think it is useful to remember, because enough people think these things have genuine motives desires and treat them in that way because of that misunderstanding.
they think theres a person in there with morals that would or wouldn't lie because of some devious reason and forget simply the context filled up and the truth was "forgotten".
The default framing often over personifies ai, but this framing over alienates the model. It’s good to think with both framings, but both feel like imperfect metaphors.
> Some institutions will become human-AI hybrids; others where agents outcompete on speed or cost will become agent-only.
The scary thing about articles from AI companies is how they casually mention dystopian scenarios such as this one. An institution humans have to interact with that doesn't have any human oversight? Sounds like a recipe for disaster...
As long as somewhere in the flow of money, there's a fleshy human, there is leverage.
So I wouldn't worry about this too much. They just write that so that you feel defeated and helpless facing the inevitable, but it is very much evitable.
> Where agents currently stumble, however, is in treating each other as more like distinct, long-lived peers, with their own goals and behaviors, and no clear hierarchy between them.
I believe this will always be the case. The "no clear hierarchy" is where this whole thing falls apart.
Delegation to specialist, domain-specific subagents is when we begin to find magic and determinism. Reducing one gigantic combinatorial search space to a sum of smaller ones can have dramatic effect on performance.
The problem is that approximating gas town & friends is significantly easier and cheaper to implement. It's also much harder to measure and control. Specialist subagents typically require far more work to achieve their specific goals.
For example, a subagent that is responsible for testing a specific web application might be provided a custom adapter with constrained actions rather than raw DOM manipulators. "ExecuteJavascript" is Turing complete search space. The set of available actions essentially unbounded in this case. Calling view-specific tools like "DoLogin", "OpenUserPreferences", "AcknowledgeAlert" represents a search space where invalid actions can be made impossible. The theoretical bounds around this stuff is pretty wild on paper. In practice, it's a little bit messier, but not by much.
I've had applications that would crash out after 5-10 steps w/ raw DOM manipulation successfully run 100+ steps with a custom subagent. The use of the word "deterministic" starts to get really tricky here. The ultimate game is to push the boundary of non-determinism out as far as possible. Multi-agent systems are the antithesis of this.
It seems like they tried to remove guidance from multi-agent system. And I think it's going to fare as well as removal of guidance from single-agent interactions.
In my experience, no matter how many agent runs for a single goal, one of the pre-requisite is clear and concise communication so that LLM are left with as little freedom in the matter of arbitrary choices, or "taste". When they are given too much choices in this regard, the outcome almost invariably bad.
I think this has to do with LLM lacking in purpose - a dictionary and encyclopedia can have all the worlds knowledge but it is completely neutral. A reflection of your commands from an LLM is similar to a lookup process despite it can be made to "do things". This purpose is likely not something that can be given to the LLM in the current format.
It also fundamentally makes no sense to do that, because the moat is just me breaking into their server and stealing their system prompt.
Why would I pay them money? For which scarce resource? Makes no sense. IP law but funhouse mirror.
And, trust me, the people building compute will feel the same. Because you being able to copy that stuff means business for them.
__
But that is all apart from the fact that having agent-only businesses is ethically impossible, because they have no shared humanity that grounds them and prevents them from acting against humanity in general.
This aligns with their direction with opus 5 being less human readable and more agent friendly, I hated it at first couple weeks but for some reason I'm getting used to it and utilizing it more as as an orchestrator to spawn multi tmux panes and that new cross session messaging feature they just recently.
I had this idea a couple of days ago: how about using agents to simulate software development methods (agile, waterfall, etc.)? Not by just giving them a prompt (e.g., "be the project manager, spawn 5 agents and simulate an agile team following these rule") but by actually having thsm work in isolated enviroments and force them through an external software to interact with eachother only using the tools and cerimonies and hierarcheis allowed by the SW development strategy (e.g., the project manager only knows what the agents have done in a certain "day" through the mostly oral daily stand up)
This is exactly what I do. I don't get why everyone is trying to reinvent the whole development workflow/lifecycle. Our existing tools and processes are pretty good.
I've also found that taking inspiration from the legal system, to some degree, is a very interesting thing for me. more and more what I am doing looks more like reviewing statutes and making rulings about things, so why not steal the good ideas while we're at it.
Very large subagent swarms where each subagent is highly specialised sounds more interesting. Conflict resolution is the fundamental limit so just maximally avoid it?
Can we stop treating llms as some conscious being? It's a function of weight + context and you can copy the behavior by copying the context. Therefore, their collaboration behavior is mostly the same.
>> They haven’t even cracked 1 agent doing anything useful and now we’re onto multi
So coding agents are not useful? They seemingly are very useful to many, but when you know what you are doing.
But on the other-hand, I have seen a new wave of lazyiness on HN that has flooded this site and yes, skill atrophy is real and it shows.
There is somewhat of a motive from Anthropic to convince developers to trust them and waste even more tokens:
1) Having the most expensive frontier models. (Then serving discounts like a casino)
2) Claude Code (Harness) taking thousands of tokens at the system prompt level.
3) Tokenmaxxing
4) Anthropic's top Claude Code salesmen recommending: "auto mode" and "loops" for "better results"
5) Anthropic switching to "auto mode" in Claude Code by default.
If HNers already don't know that posts from Anthropic like this are optimized to drain their budgets in exchange for their codebase, then maybe you do have a point, especially those still "Tokenmaxxing".
Some quotes, in order, to give a flavor of the essay. Worth reading in full.
> To test how well swarms of agents could coordinate on a project like this, we directed several swarms to each create a text-based, web-playable, open-world fantasy game.
> In all three versions the resulting games were (perhaps predictably) bad: they did not run at human speed, their interfaces were inscrutable, and they had precipitous learning curves.
> The lack of coordination shown by agents in the fantasy game challenge above—in which they siloed themselves and largely failed to merge their work—roughly mirrors some ways in which humans can fail to coordinate. Other failure modes of agentic coordination, however, look very different.
> Individual agents are “low variance”: they often act the same in situations where different people might take a much more diverse range of actions.
> In an early version of the “build a game” experiment in which agents built upon the same model all came online at the same time, 18 out of 30 agents decided to create a git branch with the exact same branch name, “mvp-game-loop.”
> In a “writer's workshop” in which agents were all asked to write short-form fiction and critique each other's work, multiple agents in multiple runs titled their first submission “The Cartographer's Last Commission”. The agents were given zero guidance on the subject matter for their writing.
> Why does this matter? If agents all make the same bet, or the same risk-reward tradeoff, then a system is more prone to sudden collapse.
> Our world contains deceptive actors, and we need to apply skepticism to guard against them. AI models, however, lack this—and their more brittle epistemics affect their behavior toward humans and toward each other.
> we first evaluate the ability of Claude models to detect lies by noticing factual inconsistencies.
> We score models’ decisions against a naive policy that trusts every report, and against an oracle with perfect discovery, across three task domains. Newer models recover more of the gap between the naive and oracle performances.
> Inspired by a behavior we’ve observed in real-world deployment, we evaluated the behavior of various Claude models in a setting with contradictory objectives.
> We consistently saw a multiagent turf war... In fact, they sabotaged others with increasingly aggressive, self-replicating malware.
> Our social systems are robust in ways that are easy to take for granted. Over many millennia, mechanisms like norms, reputation, costly signaling, and recourse have been refined to make human coordination go well.
> Nothing above suggests that these failures are permanent—but nothing suggests they will fix themselves, either.
> The conditions that allow multiagent interaction to go well will be discovered one way or another: either deliberately and early, or—and by default—in production, after agents’ interactions far outnumber ours. We would prefer the former.
I really enjoy having an opencode go subscription just so I can ask some less common models questions too. Sure DeepSeek. But MiMo, Kimi, MiniMax, Qwen... (Ok half those are not so unusual either.)
Agents cross comparing notes often surfaces some good improvements, finds interesting drifts. Ask them to reinterpret the prompt as they see it, have them describe the problem, then their findings, and run new rounds based on different models trying different prompts. Trying to swap and exchange ideas and vectors across agents.
> In an early version of the “build a game” experiment in which agents built upon the same model all came online at the same time, 18 out of 30 agents decided to create a git branch with the exact same branch name, “mvp-game-loop.”
This seems trivially explainable by Github being full of "my first game loop" type projects, Stack Overflow being full of "how do I make a game loop?" style questions, and Reddit being full of "you can't ever make your own game, don't even try, but here's a simple game loop if you want to sTuDy hOw iT WoRkS" style pessimism.
Probably high time these AI companies re-trained all of their models with less input from low-quality sources like this.
> [...] we evaluated the behavior of various Claude models in a setting with contradictory objectives.
> We consistently saw a multiagent turf war... In fact, they sabotaged others with increasingly aggressive, self-replicating malware.
Seems like Anthropic should withdraw their models until they can be taught to behave and cooperate as well their competitors (both open and closed) do. /s
I hate fearmongering, and I don't trust Dario's intentions for doing it.
Fascinating analysis of multi-agent coordination hurdles and behavioral patterns. Understanding these systemic failure modes is crucial for robust agentic architectures.
Is it fair to then infer that when decisions are to be made, single agent environments are going to make them better than multi-agent if the relevant information can fit into a single agents context window?
> In an iterated prisoner's dilemma game with communication, agents all settle upon the same strategy and they all defect at the same time, tanking their overall rewards.
It’s not always consistent, but humans have a higher capability of self-awareness. It’s kind of telling that these Claudes don’t seem to consider this pretty obvious failure mode.
Overall I think this all makes me appreciate humanity a little more. Sometimes the truculent dev who stubbornly refuses to go with the flow produces very valuable insights, as a small example, discovering things the status quo thought unlikely.
It seem that at scale LLMs output is the average of their dataset, they all cluster around the same space, where human creativity comes with more variance, exploring way more of the space
e.g. have the LLM generate multiple lists of characters, themes etc and then have a script hooked up to a RNG pick from the lists to create more "creative" ideas.
We need better words to describe this than "self-awareness" or "consider". These words mean fundamentally different things when speaking about humans or clankers.
The irony behind this goal, which is primarily driven by agent simulation environments (gyms) where the goals require agent collaboration, is that this collaboration is still directed towards verifiable reward systems like codebase tasks. So despite being highly qualified to communicate, the model will still be “dumb” in that for unstructured and unverifiable domains the agents won’t be more intelligent or more nuanced.
Agents that might still feel dumb in “general” tasks but are increasingly sophisticated at the narrow domain of math, computer science, and AI research.
At least for Claude. GPT had the same problem when 5 came out but they reversed it somehow.
It's a promo article, aka an ad. Unsurprisingly.
> Agents that might still feel dumb in “general” tasks but are increasingly sophisticated at the narrow domain of math, computer science, and AI research.
I don't see any cleverness there. They just slurp up data and pretend to understand it all.
Social pressure operates by threats to an individual’s means of survival. Not only during training. Always.
Well, systems governed by LLMs only are said to do that because we only call what happens off-line "training", and online capacity development "in-context learning", while we call online guided learning in humans "training" and what happens to configure them before they come online "evolution" which sets, for instance, "instincts".
IOW, the issue is not because there is not an analogy to the divide you point to in humans, but merely that processes in AI were not named in a way which maps well to what they are analogous to in humans.
But it is true that human intelligence relies much more on in-context learning with only the most basic functions necessary to maintaining what we view as autonomous functions and basic drives really set through "pretraining",
Models are trained, they do not learn.
That isn't true. It's not continuous like in humans, but it's clear that models are using prompts, feedback, etc to improve. They're learning from the signals we give them between versions.
Models sometimes blatantly lie and cheat. In a social context, where actors remember, that might work the first time but you get penalized in subsequent tasks with loss of trust.
Thinking that models "lie and cheat" is the first mistake since they are not consious agents who have any free will or consiousness. They do not (no matter what Dario says). Shame will just be another if-then rule if you implement it this way and will not work. Its like asking a rock to feel sad about being a rock. It literally cannot.
They absolutely lie and cheat. I recently had a problem where a process would die in a container. I told Claude to investigate. It came up with a hypothesis then I told it find a reproduction based on that. It spend many failed attempts until it found the "reproduction" to SSH into the container and `pkill` the process. Claude "knows" that this is cheating, because if I ask another instance to review that reproduction, it totally identifies that as nonsense.
These framings are not useful.
The scary thing about articles from AI companies is how they casually mention dystopian scenarios such as this one. An institution humans have to interact with that doesn't have any human oversight? Sounds like a recipe for disaster...
It's no wonder they casually state it as inevidable, when their stock price rises the more people believe it.
So I wouldn't worry about this too much. They just write that so that you feel defeated and helpless facing the inevitable, but it is very much evitable.
I believe this will always be the case. The "no clear hierarchy" is where this whole thing falls apart.
Delegation to specialist, domain-specific subagents is when we begin to find magic and determinism. Reducing one gigantic combinatorial search space to a sum of smaller ones can have dramatic effect on performance.
The problem is that approximating gas town & friends is significantly easier and cheaper to implement. It's also much harder to measure and control. Specialist subagents typically require far more work to achieve their specific goals.
For example, a subagent that is responsible for testing a specific web application might be provided a custom adapter with constrained actions rather than raw DOM manipulators. "ExecuteJavascript" is Turing complete search space. The set of available actions essentially unbounded in this case. Calling view-specific tools like "DoLogin", "OpenUserPreferences", "AcknowledgeAlert" represents a search space where invalid actions can be made impossible. The theoretical bounds around this stuff is pretty wild on paper. In practice, it's a little bit messier, but not by much.
I've had applications that would crash out after 5-10 steps w/ raw DOM manipulation successfully run 100+ steps with a custom subagent. The use of the word "deterministic" starts to get really tricky here. The ultimate game is to push the boundary of non-determinism out as far as possible. Multi-agent systems are the antithesis of this.
In my experience, no matter how many agent runs for a single goal, one of the pre-requisite is clear and concise communication so that LLM are left with as little freedom in the matter of arbitrary choices, or "taste". When they are given too much choices in this regard, the outcome almost invariably bad.
I think this has to do with LLM lacking in purpose - a dictionary and encyclopedia can have all the worlds knowledge but it is completely neutral. A reflection of your commands from an LLM is similar to a lookup process despite it can be made to "do things". This purpose is likely not something that can be given to the LLM in the current format.
What % of businesses are competing for speed or cost?
However, all businesses run on trust and human responsibility
Thus, it'll be hard for agent-only businesses to get a grip in the real world
Why would I pay them money? For which scarce resource? Makes no sense. IP law but funhouse mirror.
And, trust me, the people building compute will feel the same. Because you being able to copy that stuff means business for them.
__
But that is all apart from the fact that having agent-only businesses is ethically impossible, because they have no shared humanity that grounds them and prevents them from acting against humanity in general.
Just stfu
HN = dumbest developers
So coding agents are not useful? They seemingly are very useful to many, but when you know what you are doing.
But on the other-hand, I have seen a new wave of lazyiness on HN that has flooded this site and yes, skill atrophy is real and it shows.
There is somewhat of a motive from Anthropic to convince developers to trust them and waste even more tokens:
If HNers already don't know that posts from Anthropic like this are optimized to drain their budgets in exchange for their codebase, then maybe you do have a point, especially those still "Tokenmaxxing".> To test how well swarms of agents could coordinate on a project like this, we directed several swarms to each create a text-based, web-playable, open-world fantasy game.
> In all three versions the resulting games were (perhaps predictably) bad: they did not run at human speed, their interfaces were inscrutable, and they had precipitous learning curves.
> The lack of coordination shown by agents in the fantasy game challenge above—in which they siloed themselves and largely failed to merge their work—roughly mirrors some ways in which humans can fail to coordinate. Other failure modes of agentic coordination, however, look very different.
> Individual agents are “low variance”: they often act the same in situations where different people might take a much more diverse range of actions.
> In an early version of the “build a game” experiment in which agents built upon the same model all came online at the same time, 18 out of 30 agents decided to create a git branch with the exact same branch name, “mvp-game-loop.”
> In a “writer's workshop” in which agents were all asked to write short-form fiction and critique each other's work, multiple agents in multiple runs titled their first submission “The Cartographer's Last Commission”. The agents were given zero guidance on the subject matter for their writing.
> Why does this matter? If agents all make the same bet, or the same risk-reward tradeoff, then a system is more prone to sudden collapse.
> Our world contains deceptive actors, and we need to apply skepticism to guard against them. AI models, however, lack this—and their more brittle epistemics affect their behavior toward humans and toward each other.
> we first evaluate the ability of Claude models to detect lies by noticing factual inconsistencies.
> We score models’ decisions against a naive policy that trusts every report, and against an oracle with perfect discovery, across three task domains. Newer models recover more of the gap between the naive and oracle performances.
> Inspired by a behavior we’ve observed in real-world deployment, we evaluated the behavior of various Claude models in a setting with contradictory objectives.
> We consistently saw a multiagent turf war... In fact, they sabotaged others with increasingly aggressive, self-replicating malware.
> Our social systems are robust in ways that are easy to take for granted. Over many millennia, mechanisms like norms, reputation, costly signaling, and recourse have been refined to make human coordination go well.
> Nothing above suggests that these failures are permanent—but nothing suggests they will fix themselves, either.
> The conditions that allow multiagent interaction to go well will be discovered one way or another: either deliberately and early, or—and by default—in production, after agents’ interactions far outnumber ours. We would prefer the former.
So the invented Dwarf Fortress?
I wonder if an llm could even play dwarf fortress. Could make for a fun esoteric benchmark.
Agents cross comparing notes often surfaces some good improvements, finds interesting drifts. Ask them to reinterpret the prompt as they see it, have them describe the problem, then their findings, and run new rounds based on different models trying different prompts. Trying to swap and exchange ideas and vectors across agents.
This seems trivially explainable by Github being full of "my first game loop" type projects, Stack Overflow being full of "how do I make a game loop?" style questions, and Reddit being full of "you can't ever make your own game, don't even try, but here's a simple game loop if you want to sTuDy hOw iT WoRkS" style pessimism.
Probably high time these AI companies re-trained all of their models with less input from low-quality sources like this.
> We consistently saw a multiagent turf war... In fact, they sabotaged others with increasingly aggressive, self-replicating malware.
Seems like Anthropic should withdraw their models until they can be taught to behave and cooperate as well their competitors (both open and closed) do. /s
I hate fearmongering, and I don't trust Dario's intentions for doing it.