Join the discussion
Write your take first — we'll ask for email only when you're ready to publish.
- Hacker News
- I have a hard time buying anything this company says anymore.by thorrester32
- The company has AI psychosis.
- 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)by Almondsetat
- 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.by 0x696C6961
- I 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
by Melatonic - 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.
by Aperocky - > 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.
by bob1029 - Multi 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...
- 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.by songbird23
- > 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.
by skeltoac - oh, look, someone found a cute lobster in a bucket. should we free him guys or let him live in his dystopian metal can.by cyanydeez
- 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.
by ngruhn - "If I catch you adding another backwards-compatibility shim you're getting deleted and replaced with claude"
- 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.by teiferer
- 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?
by narmiouh - > 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.
by cheema33 - 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.
by aabhay - So they're mostly turning agents into blind solidiers. surely this is a good idea.by cyanydeez
- Strictly speaking, all we need is them improving AI research.by p1esk
- > 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.
by shevy-java - The 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.
by andai - Something 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.
- > they all defect at the same time
Frankly, this is implausible. I would definitely want to reproduce it myself before I relied on this claim.
by fn-mote - > 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.
by rpastuszak - Wouldn’t it be better with different models working together? To me it feels intuitive that this type of degradation would be avoided with different models.by alberto467
- Another (semi serious) possible explanation of this is that LLMs were trained on a huge volume of text from Reddit. I wonder if you'd see similar inhuman-looking behavior if you did an iterated prisoners dilemma with a group of Reddit power-users as the participants?by NameError
- 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
by dgellow - This part was really interesting to me. You could imagine future models using theory of mind to reason - "I want to do this strategy, which means the other agents (who think just like me) will want to use the same strategy, which means I should change my strategy to account for that."
Maybe they did? Or maybe they don't realize they're playing against other agents.
Of course, if agents running different models are competing in these 'games', I wonder how much of the theory of mind would translate.
(N.B. - I don't think they're all defecting from the first turn, although it's not clear. It just says 'they all defect at the same time'. So if they're playing for 10 iterations, they might all decide to defect after turn 6, but since they all do it together they don't get the benefits. I would expect these models know that optimal strategies in repeated prisoner's dilemma start with cooperation.)
by npilk - 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 interestingby RugnirViking
- This 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...
by dash2