10 ms·
Agents Are Not Enough
- simonw 2y agoThis paper does at least lead with its version of what "agents" means (I get very frustrated when people talk about agents without clarifying which of the many potential definitions they are using): > An agent, in the context of AI, is an autonomous entity or program that takes preferences, instructions, or other forms of inputs from a user to accomplish specific tasks on their behalf. Agents can range from simple systems, such as thermostats that adjust ambient temperature based on sensor readings, to complex systems, such as autonomous vehicles navigating through traffic. This appears to be the broadest possible definition, encompassing thermostats all the way through to Waymos.
- adpirz 2y agoYou posted on X a while back asking for a crowdsourced definition of what an "agent" was and I regularly cite that thread as an example of the fact that this word is so blurry right now.
- simonw 2y agoI really need to write that up in one place - closest I've got is this section from my 2024 review https://simonwillison.net/2024/Dec/31/llms-in-2024/#-agents-still-haven-t-really-happened-yet https://simonwillison.net/2024/Dec/31/llms-in-2024/#-agents-...
- jvans 2y agothis is a great write up, thank you
- swyx 2y agodidnt your summary https://gist.github.com/simonw/beaa5f90133b30724c5cc1c4008d0654 https://gist.github.com/simonw/beaa5f90133b30724c5cc1c4008d0... pretty much cover it?
- adpirz 2y agoWhoa missed this! Love it.
- adpirz 2y agoThis write up was also fantastic and has made the rounds at our org!
- sanjin 2y agoI took a crack at it here that tries to bridge the gap from "autonomous" which is just software to that Agentic autonomy - https://www.aiimpactfrontier.com/p/framework-for-ai-agents https://www.aiimpactfrontier.com/p/framework-for-ai-agents
- DebtDeflation 2y ago>"The two main categories I see are people who think AI agents are obviously things that go and act on your behalf—the travel agent model—and people who think in terms of LLMs that have been given access to tools which they can run in a loop as part of solving a problem." This is exactly the problem and these two categories nicely sum up the source of the confusion. I consider myself in the former camp. The AI needs to determine my intent (book a flight) which is a classification problem, extract out the relevant information (travel date, return date, origin city, destination city, preferred airline) which is a Named Entity Recognition problem, and then call the appropriate API and pass this information as the parameters (tool usage). I'm asking the agent to perform an action on my behalf, and then it's taking my natural language and going from there. The overall workflow is deterministic, but there are elements within it that require some probabilistic reasoning. Unfortunately, the second camp seems to be winning the day. Creating unrealistic expectations of what can be accomplished by current day LLMs running in a loop while simultaneously providing toy examples of it.
- ethbr1 2y agoThe language problem around agents is that most companies are describing them solely from a human/UX perspective. 'You ask it to do something, and it does it' That makes it difficult to differentiate the more critical 'how' options in the execution process. From that perspective: deterministic integrations, LLM+tools, LAM, etc are more descriptive categories, each with their own capabilities, strengths, and weaknesses. Or to put it a different way, if the term doesn't tell you what something is good and bad at, it's probably an underspecified term.
- yyyyz 2y ago[dead]
- mindcrime 2y agoIt's been blurry for a long time, FWIW. I have books on "Agents" dating back to the late 90's or early 2000's in which the "Intro" chapter usually has a section that tries to define what an "agent" is, and laments that there is no universally accepted definition. To illustrate: here's a paper from 1996 that tries to lay out a taxonomy of the different kinds of agents and provide some definitions: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=2a6b5b22556f208d50a119be1f879792e9bab47b https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&d... And another from the same time-frame, which makes a similar effort: https://www.researchgate.net/profile/Stan-Franklin/publication/221457111_Is_it_an_Agent_or_Just_a_Program_A_Taxonomy_for_Autonomous_Agents/links/0f317530ba440e7979000000/Is-it-an-Agent-or-Just-a-Program-A-Taxonomy-for-Autonomous-Agents.pdf?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19 https://www.researchgate.net/profile/Stan-Franklin/publicati...
- ethbr1 2y agoThe technical difference between agents then and agents now are the fuzzy parameter mapping capabilities of LLMs, if used. Scaling agent capability requires agents that are able to auto-map various tools. If every different tool is a new, custom integration, that must be written by a person, then we end up where we are today -- specialized agents where there exists enough demand and stability to write and maintain those integrations, but no general purpose agents. Ultimately, parameter mapping in a sane, consistent, globally-applicable way is the key that unlocks an agentic future, or a failure that leads to its demise.
- williamcotton 2y agoSo basically just the concept of feedback in a cybernetic system. https://en.wikipedia.org/wiki/Cybernetics https://en.wikipedia.org/wiki/Cybernetics
- bob1029 2y ago> The field is named after an example of circular causal feedback—that of steering a ship (the ancient Greek κυβερνήτης (kybernḗtēs)... Now that name makes a lot more sense to me.
- openrisk 2y agoWhich is also the root of the word 'government', so a government agent is doubly cybernetic in a sense
- 8338550bff96 2y agoThen is "agents" just non-spooky coded language for "cyborgs"
- throw5959 2y agoI studied cybernetics. Our teachers called us "cybernets".
- htrp 2y agoagents are the 2020s version of data science in the 2010s
- deleted 2y ago[deleted]
- Kerbonut 2y agoDo you mean that agents are being hyped in the same way data science was in the 2010s, or that they’ll have a similar impact over time? Would love to hear more of your thoughts.
- snapcaster 2y agoI think he meant it's a similarly blurry term
- curious_cat_163 2y agoYes, and the definition works reasonably well for the core arguments they are making in Section 5. I suspect they'll follow up with a full paper with more details (and artifacts) of their proposed approach.
- behnamoh 2y agoPeople have been talking about agents for at least 2 years. Remember when AgentGPT came out? How's that going so far? Agents are just LLMs with structured output, which often happens to be a JSON with info about a function arguments to be called.
- mindcrime 2y ago> People have been talking about agents for at least 2 years. WAY longer than that. What's come to the forefront specifically in the last year or two is very specific subset of the overall agent landscape. What I like to call "LLM Agents". But "Agents" at large date back to at least the 1980's if not before. For some of the history of all of this, see this page and some of the listed citations: https://en.wikipedia.org/wiki/Software_agent https://en.wikipedia.org/wiki/Software_agent > Agents are just LLMs with structured output That's only true for the "LLM Agent" version. There are Agents that have nothing to do with LLM's at all.
- simonw 2y agoRight - the term "user-agent" shows up in the HTTP/1.0 spec from 1996: https://datatracker.ietf.org/doc/html/rfc1945 https://datatracker.ietf.org/doc/html/rfc1945 and there's plenty of history of debates about the meaning of the term before then. In 1994 people were already complaining that the term that had no universal agreed definition: https://simonwillison.net/2024/Oct/12/michael-wooldridge/ https://simonwillison.net/2024/Oct/12/michael-wooldridge/
- mindcrime 2y agoYes. I am fond of saying "If you're talking about agents and think the term is something new, go back and read everything Michael Wooldridge ever wrote before talking any further". :-)
- cratermoon 2y agoWe're at the phase of the hype cycle where "agent" means whatever the marketing materials want it to mean.
- baxtr 2y agoTo me "Agents" sound like computer programs that interact through APIs?
- bsenftner 2y agoOh come on! You and I know very well an AI Agent is anything marketing says they are, and that is the absolute final truth.
- deleted 2y ago[deleted]
- bob1029 2y agoI think the goldilocks path is to make the user the agent and use the LLM simply as their UI/UX for working with the system. Human (domain expert) in the loop gives you a reasonable chance of recovering from hallucinations before they spiral entirely out of control. "LLM as UI" seems to be something hanging pretty low on the tree of opportunity. Why spent months struggling with complex admin dashboard layouts and web frameworks when you could wire the underlying CRUD methods directly into LLM prompt callbacks? You could hypothetically make the LLM the exclusive interface for managing your next SaaS product. There are ways to make this just as robust and secure as an old school form punching application.
- GiorgioG 2y agore: LLM as UI: Given that I don't trust LLMs to be deterministic, I wouldn't trust them to make the correct API call every time I tell it to do X.
- kgeist 2y agoI think most users have a fixed set of workflows which usually don't change from day to day, so why not just use LLMs as a macro builder with a natural language interface (and which doesn't require you to know the product's UI well beforehand): - you ask LLM to build a workflow for your problem - the LLM builds the workflow (macro) using predefined commands - you review the workflow (can be an intuitive list of commands, understandable by non-specialist) - to weed out hallucinations and misunderstanding - you save the workflow and can use it without any LLM agents, just clicking a button - pretty determenistic and reliable Advantages: - reliable, deterministic - you don't need to learn a product's UI, you just formulate your problem using natural language
- dingnuts 2y ago>- you review the workflow (can be an intuitive list of commands, understandable by non-specialist) so you define a DSL that the LLM outputs, and that's the real UI >- you don't need to learn a product's UI, you just formulate your problem using natural language yes, you do. You have to learn the DSL you just manifested so that you can check it for errors. Once you have the ability to review the LLM's output, you will also have the ability to just write the DSL to get the desired behavior, at which point that will be faster unless it's a significant amount of typing, and even then, you will still need to review the code generated by the LLM, which means you have to learn and understand the DSL. I would much rather learn a GUI than a DSL. You haven't removed the UI, nor have you made the LLM the UI, in this example. The DSL ("intuitive list of commands.. I guess it'll look like the Robot Framework right? that's what human-readable DSLs tend to look like in practice) is the actual UI. This is vastly more complicated than having a GUI to perform an action.
- TaurenHunter 2y ago"More Agents is all you need" https://arxiv.org/abs/2402.05120 https://arxiv.org/abs/2402.05120 I could not find a "Agents considered harmful" related to AI, but there is this one: "AgentHarm: A benchmark for measuring harmfulness of LLM agents" https://arxiv.org/pdf/2410.09024 https://arxiv.org/pdf/2410.09024 This "Agents considered harmful" is not AI-related: https://www.scribd.com/document/361564026/Math-works-09 https://www.scribd.com/document/361564026/Math-works-09
- kridsdale1 2y agoMorpheus taught me they are quite harmful.
- ksplicer 2y agoWhen reading anthropics blog on agents I basically took away that their advice is you shouldn't use them to solve most problems. https://www.anthropic.com/research/building-effective-agents https://www.anthropic.com/research/building-effective-agents "For many applications, however, optimizing single LLM calls with retrieval and in-context examples is usually enough."
- retinaros 2y agoTrue this was also my conclusion in October. Most of the complexity we are building is to fight against the limitations of LLMs. If in some way we could embed all our tools in a single call and have the LLM successfully figure out which tools to call then that would be it and we wouldn’t need any of those frameworks or libraries. But it turns out the reality of agents and tool use is pretty stark and you wouldn’t know that looking at the AI influencer spamming X, Linkedin, Youtube However The state of agents slightly changed and while we had 25% accuracy in multiturn conversations we re now at 50.
- dist-epoch 2y agoReal agents have never been tried
- sgt101 2y agoHi - have a look at this book if you are interested [1] (Mike Wooldridge, Multi-Agent Systems) [1] https://amzn.eu/d/6a1KgnL https://amzn.eu/d/6a1KgnL Here are Mike's credentials :https://www.cs.ox.ac.uk/people/michael.wooldridge/ https://www.cs.ox.ac.uk/people/michael.wooldridge/
- zombiwoof 2y agoAgent is a funding and marketing term imho Soon it will be AI Microservices
- bad_haircut72 2y agoWho wants to invest in my startup, its a Microagent service architectures orchestration platform. All you do is define the inputs, write the agents algorithms, apply agency by inputting a decision tree (ifs and conditionals) and then a function to format output! And the best part? You do all of it in YAML! /sarcasm, hopefully obviously
- mindcrime 2y agoI was thinking "shut up and take my money" until you brought YAML into it. Hard pass. ;p
- deleted 2y ago[deleted]
- ramesh31 2y ago>Agent is a funding and marketing term imho So was "mobile" 15 years ago. Companies are deploying hundreds of billions in capital for this. It's not going anywhere, and you'd be best off upskilling now instead of dismissing things.
- nowittyusername 2y agoWith time, they will get a lot better. IMO, the biggest hurdles the agents currently lack is good implementation of function calling capabilities. LLM's should be used as reasoning engines and everything else should be offloaded to tool use. This will drastically reduce hallucinations and errors in math and all the other areas.
- lionkor 2y agoDo they reason, though?
- deleted 2y ago[deleted]
- ripped_britches 2y agoI can imagine really powerful agents this year or next in theory. Agents meaning (not a thermostat) a system that can go complete some async tasks on your behalf. But in practice I don’t have any idea how we will solve for prompt injection attacks. Hopefully someone cracks it.
- cratermoon 2y ago"AI will soon be able too..."
- Jerrrry 2y ago>solve for prompt injection attacks It is essentially the same Code as Data problem as always.
- georgestrakhov 2y agoIMHO, the word agent is quickly becoming meaningless. The amount of agency that sits with the program vs. the user is something that changes gradually. So we should think about these things in terms of how much agency are we willing to give away in each case and for what gain[1]. Then the ecosystem question that the paper is trying to solve will actually solve itself, because it is already the case today that in many processes agency has been outsourced almost fully and in others - not at all. I posit that this will continue, just expect a big change of ratios and types of actions. [1] https://essays.georgestrakhov.com/artificial-agency-ladder/ https://essays.georgestrakhov.com/artificial-agency-ladder/
- w10-1 2y ago> IMHO, the word agent is quickly becoming meaningless. The amount of agency that sits with the program vs. the user is something that changes gradually Yes, the term is becoming ambiguous, but that's because it's abstracting out the part of AI that is most important and activating: the ability to work both independently and per intention/need. Per the paper: "Key characteristics of agents include autonomy, programmability, reactivity, and proactiveness.[...] high degree of autonomy, making decisions and taking actions independently of human intervention." Yes, "the ecosystem will evolve," but to understand and anticipate the evolution, one needs a notion of fitness, which is based on agency. > So we should think about these things in terms of how much agency are we willing to give away in each case It's unclear there can be any "we" deciding. For resource-limited development, the ecosystem will evolve regardless of our preferences or ethics according to economic advantage and capture of value. (Manufacturing went to China against the wishes of most everyone involved.) More generally, the value is AI is not just replacing work. It's giving more agency to one person, avoiding the cost and messiness of delegation and coordination. It's gaining the same advantages seen where smaller team can be much more effective than a larger one. Right now people are conflating these autonomy/delegation features with the extension features of AI agents (permitting them to interact with databases or web browsers). The extension vendors will continue to claim agency because it's much more alluring, but the distinction will likely become clear in a year or so.
- paulryanrogers 2y ago
- cratermoon 2y agoHere's a link to arxiv page for the paper, in case you want to look over the abstract and citation metadata before downloading the PDF. https://arxiv.org/abs/2412.16241 https://arxiv.org/abs/2412.16241
- jokethrowaway 2y agoI don't get the hype about Agents. It's just calling a LLM n-times with slightly different prompts Sure, you get the ability to correct previous mistakes, it's basically a custom chain of thought - but errors compound and the results coming from agents have a pretty low success rate. Bruteforcing your way out of problems can work sometimes (as evinced by the latest o3 benchmarks) but it's expensive and rarely viable for production use.
- grahamj 2y ago> It's just calling a LLM n-times with slightly different prompts It can be, but ideally each agent’s model, prompts and tools are tailored to a particular knowledge domain. That way tasks can be broken down into subtasks which are classified and passed to the agents best suited to them. Agree RE it being bruteforce and expensive but it does look like it can improve some aspects of LLM use.
- retinaros 2y agoThat is just like having a for loop per domain.
- mindcrime 2y ago> It's just calling a LLM n-times with slightly different prompts That's one way of building something you could call an "agent". It's far from the only way. It's certainly possible to build agents where the LLM plays a very small role, or even one that uses no LLM at all.
- retinaros 2y agoThats a workflow
- coro_1 2y agoThe paper covers technical details and the logistics of AI Agents to come. But how are humans going to react to mass AI Agents replacing other human emotion and connection? Bias is central in tech-culture to only agents, but this could become an issue.
- danielmarkbruce 2y agoWhy post this paper? It says nothing, it's a waste of people's time to read.
- duxup 2y agoEven just the definition of an Agent (maybe imperfect) made it worthwhile for me.
- danielmarkbruce 2y agoI'm not sure it's even good though... the input doesn't need to come from a user. I have an "agent" which listens for an event in financial markets and then goes and does some stuff. In practice the current usage of "agent" is just: a program which does a task and uses an LLM somewhere to help make a decision as to what to do and maybe uses an LLM to help do it.
- sgt101 2y agoHi - have a look at this book if you are interested [1] (Mike Wooldridge, Multi-Agent Systems) [1] https://amzn.eu/d/6a1KgnL https://amzn.eu/d/6a1KgnL Here are Mike's credentials :https://www.cs.ox.ac.uk/people/michael.wooldridge/ https://www.cs.ox.ac.uk/people/michael.wooldridge/
- j45 2y agoMath that can't be too warm and too accurate to work may have challenges being too accurate and reliable with repeating processes.
- pwillia7 2y agoHow would the SIMS that contain the user prefs and whatnot not have the same issues described in the paper as the agents themselves?
- tonetegeatinst 2y agoSomewhat related but here's my take on super intelligence or AGI. I have worked with CNN,GNN and other old school AI methods, but don't have the resources to build a real SOT LLM, but I do use and tinker with LLM's occasionally. If AGI or SI(super intelligence)/is possible, and that is an if...I don't think LLM's are going to be this silver bullet solution Just as we have in the real world of people who are dedicated to a single task in their field like a lawyer or construction workers or doctors and brain surgeons, I see the current best path forward as being a "mixture of experts". We know LLM's are pretty good for what iv seen some refer to as NLP problems, where the model input is the tokenized string input. However I would argue an LLM will never built a trained model like stockfish or deepseek. Certain model types seem to be suited to certain issues/types of problems or inputs. True AGI or SI would stop trying to be a grand master of everything but rather know what best method/model should be applied to a given problem. We still do not know if it is possible to combine the knowledge of different types of neural networks like LLMs, convolutional neural networks, and deep learning...and while its certainly worth exploring, it is foolish to throw all hope on a single solution approach. I think the first step would be to create a new type of model where given a problem of any type. It knows the best method to solve it. And it doesn't rely on itself but rather the mixture of agents or experts. And they don't even have to be LLMs. They could be anything. Where this really would explode is, if the AI was able to identify a problem that it can't solve and invent or come up with a new approach, multiple approaches, because we don't have to be the ones who develop every expert.
- wkat4242 2y agoTotally agree. An LLM won't be an AGI. It could be part of an AGI, specifically the human interface part. That's what an LLM is good at. The rest (knowledge oracle, reasoning etc) are just things that kinda work as a side-effect. Other types of AI models are going to be better at that. It's just that since the masses found that they can talk to an AI like a human they think that it's got human capabilities too. But it's more like fake it till you make it :) An LLM is a professional bullshitter.
- lugu 2y agoI am not sure what you mean by LLM when you say they are professional bullshitter. While it was certainly true for model based on transformers just doing inference, recent models have progressed significantly.
- syntex 2y agoWhy does this have so many upvotes? Is this the current state of research nowadays?
- ocean_moist 2y agoMaybe I just don’t understand the article but I really have 0 clue how they go about making their conclusions and really don’t understand what they are saying. I think the 5 issues they provide under “Cognitive Architectures” are severely underspecified to the point where they really don’t _mean_ anything. Because the issues are so underspeficifed I don’t know how their proposed solution solves their proposed problems. If I understand it correctly, they just want agents (Assistants/Agents) with user profiles (Sims) on an app store? I’m pretty sure this already exists on the ChatGPT store. (sims==memories/user profiles, agents==tools/plugins, assistants==chat interface) This whole thing is so broad and full of academic (pejorative) platitudes that it’s practically meaningless to me. And of course although completely unrelated they through a reference into symbolic systems. Academic theater.
- spiderfarmer 2y agoThis is publishing for the sake of publishing.
- sambo546 2y agoThe general negativity toward agents makes it read like the problem section of a research proposal ("X isn't good enough, we're going to develop solution Y").
- spiderfarmer 2y agoThat’s exactly what I thought.
- antisthenes 2y agoIt's a 4-page paper trying to give a summary of 40+ years of research on AI. Of course it's going to be vague and presumptuous. It's more of a high-level executive summary for tech-adjacent folks than an actual research paper.
- DebtDeflation 2y agoThis whole idea of prompting an LLM and piping the output as the input (prompt) of another LLM and asking it to do something with it (like critique/edit it) and then piping the output of that LLM back to the first LLM along with instructions to keep repeating the process until some stop criteria is met seems to me to just be a money-making scheme to drive up token consumption.
- deleted 2y ago[deleted]
- beezle 2y agoFor those who dont want to down load the PDF directly and prefer to start with the abstract: https://arxiv.org/abs/2412.16241 https://arxiv.org/abs/2412.16241
- asciii 2y agoDiabolical - I love it. Impressed that the final score came up as an alert!
- joshka 2y agohttps://www.arxiv.org/abs/2412.16241 https://www.arxiv.org/abs/2412.16241 is the non-pdf version of this @dang can you please replace the link?
- dang 2y agoOk! changed from https://www.arxiv.org/pdf/2412.16241 https://www.arxiv.org/pdf/2412.16241.
- rcarmo 2y agoThis reads a lot like agents wearing different kinds of trenchcoats (or underwear). Can’t really see an evidence this separation works.
- authorfly 2y agoDoes anyone else get the sense that the definition has been bastardized by the conflation of the two concurrent previous uses of "agent"? i.e. in AI, biology and informatics, "Agent" typically meant something: That had a form / self / embodiment. That could sense the environment and react to those perceptions. That possibly could learn, adapt, or change to various degrees of complexity, which would entail optionally being an "intelligent system". Meanwhile in common parlance, Agent meant: Someone who acts or behaves on behalf of another adaptively to accomplish something with some degree of freedom. And this might explain why so people say agent/agentic necessarily refers to "tool use" or "being able to overcome problems on the happy path" or "something capable of performing actions on an infinite loop while reacting" (the latter two in my opinion, conflates the meaning of "Intelligent system" or "Intelligent behavior"). Meanwhile, biologists might still reply to a single cell seemingly inert, or a group of bacteria in a colony, as an Agent (a more behaviouralist/chemical "look-deep-down" perspective) I think a lot of disappointment is that biologists/OG AI enthusiasts are looking for something truly adaptive, sensing, able to behave, "live" indefinitely, have acquire or set goals, and which might be able to if intelligent, work with other agents to accomplish things (e.g. a "society"). Meanwhile, people who just want an "AI HR Agent" just want something that can communicate, interview, discern good applicants, and book the interviews plus provide summary notes. These two things are very different. But both, could use tools etc (the key difference from ChatGPT which is enabling this new concept to be more useful than ChatGPT, alongside various forms of short term memory rather than "fresh-every-time-conversations).