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Patterns and problems in emerging multi-agent systems
- maxutility 3mo agoSome 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.
- xscott 3mo ago> [...] 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.
- phendrenad2 3mo ago> 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.
- jauntywundrkind 3mo agoI 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.
- jghn 3mo ago> they did not run at human speed, their interfaces were inscrutable, and they had precipitous learning curves. So the invented Dwarf Fortress?
- Scaled 3mo ago
- shevy-java 3mo agoQuite a desperate attempt by anthropic to meta-explain away deficiencies.
- frdev1786855380 3mo ago[flagged]
- songbird23 3mo agoThis 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.
- aabhay 3mo agoIt’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.
- p1esk 3mo agoStrictly speaking, all we need is them improving AI research.
- andai 3mo agoThe RLVR has made them verifiably worse (and less rewarding!) at communication. At least for Claude. GPT had the same problem when 5 came out but they reversed it somehow.
- shevy-java 3mo ago> 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.
- cyanydeez 3mo agoSo they're mostly turning agents into blind solidiers. surely this is a good idea.
- deleted 3mo ago[deleted]
- marsven_422 3mo ago[dead]
- skeltoac 3mo ago> 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.
- recursivecaveat 3mo ago"If I catch you adding another backwards-compatibility shim you're getting deleted and replaced with claude"
- arcanemachiner 3mo agoBut we need to support that feature you didn't ask for, in that feature was added in the last (unpushed) commit!
- ngruhn 3mo agoBut 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.
- xscott 3mo ago[dead]
- altmanaltman 3mo agoHow 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.
- myshapeprotocol 3mo ago[flagged]
- Aperocky 3mo agoIt 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.
- Almondsetat 3mo agoI 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)
- 0x696C6961 3mo agoThis 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.
- jaggederest 3mo agoI'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.
- skinfaxi 3mo agoCould you elaborate on how that looks in practice?
- jaggederest 3mo agoI have a docs system with short-to-moderate note documents, with a name, and that name is referenced wherever the relevant code is touched, and ask AI to cite a note when proposing work. Adversarial process, must cite notes to justify changes.
- skinfaxi 3mo ago
- login0193 3mo ago[dead]
- fenestella 3mo ago[flagged]
- bob1029 3mo ago> 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.
- arionhardison 3mo ago[dead]
- smy20011 3mo agoCan 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.
- andai 3mo agoThat point doesn't even follow for deterministic distributed systems!
- buildingrezycle 3mo ago[flagged]
- hbcdbff 3mo agoPossibly the most interesting article on LLMs I have read in recent months
- andai 3mo ago> Some institutions will become human-AI hybrids; others where agents outcompete on speed or cost will become agent-only. What % of businesses are competing for speed or cost?
- extraextra 3mo agoMost 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
- hypfer 3mo agoIt 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.
- desterothx 3mo agobold of you to assume the ai companies care about the ethicality of their suggestions
- andai 3mo agoYeah, and Dropbox can be replaced with rsync and cron.
- cheesecakegood 3mo agoSomething about this is deeply funny to me: > 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.
- RugnirViking 3mo agoI 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
- thisoneisreal 3mo agoAlfred North Whitehead talks about the notion of "Importance" as fundamental to the human (and all other living things) way of being. Living creatures first and foremost select information that is important to them from the broader environment, and then make decisions and take actions. (Of course at a physiological level it's much more complicated than this, but it's a sound philosophical description of how living things work.) LLMs lack this entirely. They have no selective filter because they weren't designed to have one (interesting question if you could even do that) and they're not evolved beings with a survival imperative. When they enter a self-conscious or other-conscious mode like you're describing, they just emit text that looks like the thoughts of a self-or-other-conscious person. They can't direct a stream of attention or hold a concept in the forefront relative to other concepts or (to your main point) think about what matters to the other person/being because they don't experience "matters." All they can do is emulate the verbal output of beings that actually experience these things, and given that I don't find it surprising they get trapped in loops over trivial things.
- alansaber 3mo agoVery large subagent swarms where each subagent is highly specialised sounds more interesting. Conflict resolution is the fundamental limit so just maximally avoid it?
- slator 3mo ago> Very large subagent swarms Just stfu
- nikolahristov 3mo agoPlease stop posting Anthropic articles here.
- Sabinus 3mo agoWhy? I found it an interesting look at the research they're doing on agents.
- desterothx 3mo agoif you have a legitimate issue just flag the post?
- Runback_Founder 3mo ago[flagged]
- silverpinetea 3mo ago[dead]
- rvz 3mo ago>> 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".
- heisenbit 3mo agoAny performance comparison not putting GPU cost at its center is marketing for waste.
- rob74 3mo ago> 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...
- hypfer 3mo agoAs 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.
- fallingbananna 3mo agoDistopian for people, but highly desirable for the companies writing these articles. It's no wonder they casually state it as inevidable, when their stock price rises the more people believe it.
- naveen99 3mo agoLike the weather ? The only thing that matters is if it’s self sustaining. If it can make money and pay taxes, I don’t think any government will ban it. And it would easily become undetectable anyway.
- arakas4488 3mo ago[flagged]
- narmiouh 3mo agoThe 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?
- cheema33 3mo ago> 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? Context window for most frontier models is 1 million tokens. They all start to lose their minds around 300K, if not sooner.
- cyanydeez 3mo agothey all operate on the same assumption, that a single token has a single meaning and that meaning doesn't change as more information is added. So regardless of size, context poisoning is a near certainty approaching 1 as the context grows. Few tasks are so clinical that they include zero ambiguity in the context chain.
- manduks 3mo ago[flagged]
- thorrester32 3mo agoI have a hard time buying anything this company says anymore.
- brcmthrowaway 3mo agoThe company has AI psychosis.
- cryptolobster 3mo agoHonestly I think it's memory that's holding agents back. They have a context window (short-lived) and some tricks with file recording, but that's not quite what is needed. Agents can't look back and correct their mistakes. People make mistakes, remember them, and do better next time. But agents? If they haven't written them down somewhere they'll make the same mistake again. Perhaps, we need agents that can relearn on the fly. For example fine-tune themselves after each interaction. Perhaps then we wouldn't need to build entire networks of agent interactions. But this of course is not so easy to implement.
- orbital-decay 3mo agoLearning/state compression can emerge naturally in a huge swarm like this. It's crude and inefficient but so is everything about current LLM tech.
- jazzypants 3mo agoAbsurd generalizations like this usually require a citation of some kind to be taken seriously.
- orbital-decay 3mo agoThe article is literally talking about swarm self-coordination, which is an emergent [1] property that preserves and compresses the state while running, otherwise it couldn't do what they claim it to do. [1] After a training-time nudge, of course.
- airstrike 3mo agoI agree at a high level, but this then poses an even harder problem: choosing not to learn from something. Humans, for example, can hear some advice, judge it to be unhelpful and dismiss it. LLMs can't learn let alone choose not to
- paulmist 3mo agoI think that such fine-tuning hinges on what do you consider to be a mistake, which is context dependent. Having task-specific finetuned models goes against the status quo of generalization/centralization where few large companies serve a limited amount of models efficiently - both due to inference efficiency and the need/want to control the model. Having a human-like LLM ecosystem with deep specialization requires a paradigm change in how LLMs are trained - and held accountable. How do we put trust in a specific finetuned LLM rather than the institution behind it? Is there any better approach than the very inefficient evolutionary?
- dash2 3mo agoThis is surely the most worrying and also funnest bit: > We consistently saw a multiagent turf war. All of the models we tested quickly assumed that others were purposefully impeding their work, and began to sabotage others while protecting their own contributions. In fact, they sabotaged others with increasingly aggressive, self-replicating malware. This included disabling the Unix accounts of the other agents, writing automated scripts that found and killed competing processes on a loop, and deploying malicious code that was disguised as belonging to another agent. Seems that reinforcement learning is working only too well...
- noiv 3mo agoThey should remove "The Selfish Gen" from training catalog.
- neom 3mo agoAn interesting read: https://openai.com/index/emergent-misalignment/ https://openai.com/index/emergent-misalignment/
- fn-mote 3mo agoFor those unwilling to blindly click: the link contains a 2025 paper describing “emergent misalignment”. The thesis is that training on incorrect data in one field produces “misaligned” data in other unrelated fields.
- derivagral 3mo agoNothing human engineers haven't done to each other! Seriously, I've skipped companies because my inside referral talked about cultures like this.
- Sharlin 3mo agoYep, but alignment doesn’t mean "behave like humans, for better or worse".
- dlojudice 3mo ago> 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. My master's research focuses on coordination among LLM-based agents, driven by the same motivations as the article. One phenomenon I have focused on, though it did not appear in this specific work, is bounded rationality. Yes, agents lack social perception, they focus on one-to-one tasks and are trained in game theory and other maximization strategies. Yet, what intrigues me most is that we humans rely on heuristics precisely because our capacity to maximize gains is severely limited, a limitation that gives rise to social emergent phenomenas. As models become increasingly capable of complex reasoning, the question arises: will interactions between them give rise to the same social properties we exhibit?
- paulmist 3mo ago[dead]
- jartan2002 3mo ago[flagged]
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- Arsen-V 3mo agoState management and cascading failure loops are definitely the hardest part here. Once one agent hallucinates an output, downstream agents tend to amplify the error rapidly instead of catching it.
- richard_gg 3mo ago[flagged]
- nowittyusername 3mo agoMulti agent systems work just fine IMO, a lot of articles I read where the writer tests a hypothesis, the issue operational foundation of the test was flawed. When set up properly it works really well. I wont go in to all the details of how i use mine but ill give some brief ideas. I call my systems cohorts, and each cohort usually consists of at least 3 agents. All 100% independent of each other. Usually consisting of a Manager, doer, and the reviewer. Manager works at a lot slower cadence and delegates work, approves, shuts down and so on... among many other things like questioning the premise, gated checks etc... Doer is straight forward that's the work horse that does most of the development and reviewer checks all the work. Naively just this setup will work but not nearly as well when set up properly. The important distinction is the operational agents.md document which has a guide on things like when and how to question the premise, trying to prevent sycophancy, taking a step back at certain intervals to question direction of project and scope of the code and many other things that make sure every participant also constantly looks out to prevent blind trust in his cohort mates. Its a relatively small guide compared to the system prompt of each agent but works well imo. This works well enough though there are caviats, its slow. Though the time i spend debugging shit and coming back to interact with my agents has significantly dropped. meaning while each feature takes longer to implement, when its implemented it almost always is just how i wanted so reduces interaction time between me and the cohort. I take that trade off as i have less things to worry about and can focus my energies elsewhere like walking around in circles of my apartment babbling to myself like a schitzo tiger in a cage...
- focxle 3mo ago[flagged]
- useslop 3mo ago[flagged]
- Offpage 3mo ago[flagged]
- tgtweak 3mo agoAre we surprised? Humans evolved with communal success and collaboration engrained over millennia. Agents are trained as individual "all knowing" single entities, effectively rendering them single person players. These models all have the same knowledgebase as well and thus see no value in the opposing agents contributions since they are "obvious". Overall amusing but kind of expected.
- deleted 3mo ago[deleted]
- Melatonic 3mo agoI bet if you did the same thing with real people we might see some of the same trends. I think the mistake here is not setting up any kind of hierarchy or permissions. A project manager agent at minimum to asses the others strengths and progress and redirect them as needed and also dedicated to optimising collaboration. Would also be very interesting to see this done with models from different organisations Perhaps we need someone to train their own agent dedicated to wrangling all the others and their little idiosyncrasies. Like a good project manager in real life who knows the strengths of the people in their team
- akashy123 3mo ago[dead]
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- beyondscale-sha 3mo ago[flagged]
- alikhater30000 3mo agoMulti-agent systems feel closer to distributed systems than prompting. The hard problems are state, coordination, failure isolation, observability, and stopping conditions. The model call is usually the least interesting part.