17 ms·

AI agents: Less capability, more reliability, please

• rglover 2y ago
  > Given the intensifying competition within AI, teams face a difficult balance: move fast and risk breaking things, or prioritize reliability and risk being left behind. Can we please retire this dichotomy? Part of why teams do this in the first place is because there's this language of "being left behind." We badly need to retreat to a world in which rigorous engineering is applauded and expected—not treated as a nice to have or "old world thinking."
• getnormality 2y ago
  "Less capability, more reliability, please" is what I want to say about everything that's happened in the past 20 years. Of everything that's happened since then, I'm happy to have a few new capabilities: smartphones, driving directions, cloud storage, real-time collaborative editing of documents. I don't need anything else. And now I just want my gadget batteries to last longer, and working parental controls on my kids' devices.
• janalsncm 2y ago
  I think many people share the same sentiment. We don’t need agents that can kind of do many things. We need reliable programs that are really good at doing a single thing. I said as much about Manus when it came out. https://news.ycombinator.com/item?id=43350950 https://news.ycombinator.com/item?id=43350950 There are mistakes in the Manus demo if you actually look at it. And with so many AI demos, they never want you to look too closely because the thing that was created is fairly mediocre. No one is asking for the tsunami of sludge except for VCs apparently.
• YetAnotherNick 2y ago
  I think the author is doing apples to oranges comparison. If you have AI acting agnatically, capability is likely positively correlated with reliability. If you don't have AI agents, it is more reliable. AI agents are not there yet and even cursor has agent mode not selected by default. I have seen cursor agent quite a bit worse that the raw model with human selected context.
• bendyBus 2y ago
  "If your task can be expressed as a workflow, build a workflow". 100% true but the thing all these 'agent pattern' or 'workflow' diagrams miss is that real tasks require back-and-forth with a user, not just a Rube Goldberg machine that gets triggered in response to a _single user message_. What you need is not 'tool use' but something like 'process use'. This is what we did at Rasa, giving you a declarative way to define multi-step processes. An LLM lets you have a fluent conversation, but the execution of the task is pre-defined and deterministic: https://rasa.com/docs/learn/concepts/calm/ https://rasa.com/docs/learn/concepts/calm/ The fact that every framework starts with a `while` loop around an LLM and then duct-tapes on some "guardrails" betrays a lack of imagination.
• wg0 2y ago
  Totally agree with author here. Also, reliability is pretty hard to achieve when the underlying models are all mountains of probability that no one yet understands how they do what they exactly do and how to precisely fix a problem without affecting other parts. Here's CNBC Business is pushing greed that these aren't AI wrappers but next best thing after fire, bread and axe[0] [0]. https://youtu.be/mmws6Oqtq9o https://youtu.be/mmws6Oqtq9o
  • freeamz 2y ago
    same can be said about digital tech/infrastructure in general!
    • wg0 2y ago
      I can't say that based on what I know about both.
• cadamsdotcom 2y ago
  Models aren’t great at deciding whether an action is irreversible - and thus whether to stop to ask for input/advice/approval. Hence agentic systems usually are given a policy to follow. Perhaps the question “is this irreversible?” should be delegated to a separate model invocation. There could be a future in which agentic systems are a tree of model and tool invocations, maybe with a shared scratchpad.
• genevra 2y ago
  I agree up until the coding example. If someone doesn't know about version control I don't think that's any fault of the company trying to stretch the technology to its limits and let people experiment. Cursor is a really cool step in a direction, and it's weird to say we should clamp what it's doing because people might not be competent enough to fix its mistakes.
• kuil009 2y ago
  It's natural to expect reliability from AI agents — but I don't think Cursor is a fair example. It's a developer tool deeply integrated with git, where every action can have serious consequences, as in any software development context. Rather than blaming the agent, we should recognize that this behavior is expected. It’s not that AI is uniquely flawed — it's that we're automating a class of human communication problems that already exist. This is less about broken tools and more about adjusting our expectations. Just like hunters had to learn how to manage gunpowder weapons after using bows, we’re now figuring out how to responsibly wield this new power. After all, when something works exactly as intended, we already have a word for that: software.
  • bigfishrunning 2y ago
    Lol software is a field that pretty severely lacks rigor -- if software is "something that works exactly as intended", then you've had a very different experience in this industry then I have.
    • kuil009 2y ago
      Garbage in, garbage out. Like it or not, even someone’s trashy intentions can run exactly as designed — so I guess we’ve had the same experience.
• amogul 2y ago
  Reliability, consistency and accuracy is the next frontier that we all have to tackle it sucks. Friend of mine is building Empromptu.ai to tackle exactly this. From what she told me built a model where that let's you define accuracy based on your use case and their models optimize your whole system towards it.
• fullstackwife 2y ago
  Are we reinventing software engineering? What happened to the "write code for error" principle?
• BrenBarn 2y ago
  Seems related to another recent post: https://news.ycombinator.com/item?id=43542259 https://news.ycombinator.com/item?id=43542259 I tend to think that what this article is asking for isn't achievable, because what people mean by "AI" is precisely "we don't know how it works". An analogy I've used sometimes when talking with people about AI is the "I know a guy" situation. Someone you know comes and tells you "I know a guy who can do X for you", where "do X" is "write your class paper" or "book a flight" or "describe what a supernova is" or "invest your life savings". In this situation, the more important the task, the more you would probably want to know about this "guy". What are his credentials? Has he done this before? How often has he failed? What were the consequences? Can he be trusted? Etc. The thing that "a guy" and an AI have in common is that you don't know what they're doing. Where they differ is in your ability to gradually gain knowledge. In real life, "know a guy" situations become transformed into something more specific as you gain information about who the person is and how they do what they do, and especially as you understand more about the system of consequences in which they are embedded (e.g., "if this painter had ruined many people's houses he would have been sued into oblivion, or at least I would have heard about it"). And also real people are unavoidably embedded in the system of physical reality which imposes certain constraints that bound plausibility (e.g., if someone tells you "I know a guy who can paint your entire house in five seconds" you will smell a rat). Asking for "reliability" means asking for a network of causes and effects that surrounds and supports whatever "guy" or AI you're relying on. At this point I don't see any mechanism to provide that other than social and ultimately legal pressure, and I don't see any strong action being taken in that direction.
• gcp123 2y ago
  I've spent the last six months building a coding agent at work, and the reliability issues are killing us. Our users don't want 'superhuman' results 10% of the time - they want predictable behavior they can trust. When we tried the 'full agent' approach (letting it roam freely through our codebase), we ended up with some impressive demos but constant production incidents. We've since pivoted to more constrained workflows with human checkpoints, and while less flashy, user satisfaction has gone way up. The Cursor wipeout incident is a perfect example. It's not about blaming users who don't understand git - it's about tools that should know better. When I hand my code to another developer, they understand the implied contract of 'don't delete all my shit without asking.' Why should AI get a pass? Reliable > clever. It's the difference between a senior engineer who delivers consistently and a junior who occasionally writes brilliant code but breaks the build every other week."
• revskill 2y ago
  Ai can uhnderstand its output.
• rcdwealth 2y ago
  [dead]
• techblaze3 2y ago
  Appreciate the effort in writing this.
• aucisson_masque 2y ago
  Can you actually make the LLM more reliable tho ? As far as I know, llm hallucinations are inherent to them and will never be completely removed. If I book a flight, i want 100,0% reliability, Not 99% ( which we are still far away today). People got to take llm for what they are, good bullshiter, awesome to translate text or reformulate words but it's not designed to have thought or be an alternate secretary. Merely a secretary tool.
• dev_susan30 2y ago
  [dead]
• whatnow37373 2y ago
  Agents introduce causality, reflection, necessity and various other sub-components never to be found in purely stochastic completion engines. This is an improvement, but it does require breaking down what each "agent" needs to do. What are the "core components" of cognition? That's why I claim that any sufficiently complicated cognitive architecture contains an ad hoc, informally-specified, bug-ridden, slow implementation of half of Immanuel Kant's work.
• bobosha 2y ago
  i think this agents vs workflow is a false dichotomy. A workflow - at least as I understand it - is the atomic unit of an agent i.e an agent stitches workflow(s) together.
• NAHWheatCracker 2y ago
  [flagged]
• cryptoz 2y ago
  This is refreshing to read. I, like everyone apparently, am working on my own coding agent [1]. And I suppose it's not that capable yet. But it sure is getting more reliable. I have it only modify 1 file at a time. It generates tickets for itself to complete - but never enough tickets to really get all the work done. The tickets it does generate, however, it often can complete (at least, in simple cases haha). The file modification is done through parsing ASTs and modifying those, so the AI doesn't go off and do all kinds of things to your whole codebase. And I'm so sick of everything trying for 100% automation and failing. There's a place for the human in the loop, in quickly identifying bugs the AI doesn't have the context for, or large-scale vision, or security or product-focused mindset, etc. It's going to be AI and humans collaborating. The solutions that figure that out the best are going to win IMO. AI won't be doing everything and humans won't be doing it all either. The tools with the best human-AI collaboration are where it's at. [1] https://codeplusequalsai.com https://codeplusequalsai.com
  • helltone 2y ago
    How do you modify ASTs?
    • cryptoz 2y ago
      I support HTML, JS, Python and CSS. For HTML, (not technically an AST), I give the LLM the original-file HTML source, and then I instruct it to write python code that uses BeautifulSoup to modify the HTML. Then I get the string back from python of the full HTML file, modified according to the user prompt. For python changes I use ast and astor packages, for JS I use esprima/escodegen/estraverse, and for CSS I use postcss. The process is the same for each one: I give the original input souce file, and I instruct the LLM to parse the file into AST form and then write code that modifies that AST. I blogged about it here if you want more details! https://codeplusequalsai.com/static/blog/prompting_llms_to_modify_existing_code_using_asts.html https://codeplusequalsai.com/static/blog/prompting_llms_to_m...
      • skydhash 2y ago
        I took a look at your project and while it's nice (technically), for the actual use case shown, I can't see the value over something like the old Dreamweaver with a bit of training. I still think like prompting is still the wrong interface for programming systems. Even though they're restricted, configurations forms, visual programming with nodes, and small scripts attached to objects on a platform is way more reliable and useful.
• dfxm12 2y ago
  Google Flights already nails this UX perfectly Often when using an AI agent, I think to myself that a web search gets me what I need more reliably and just as quick. Maybe AI has to learn to crawl before it learns to walk, but each agent I use is leaving me without confidence that it will ever be useful and I genuinely wonder if they've ever been tested before being published...
  • monero-xmr 2y ago
    Assume humans can do anything in a factory. So we create a tool to increase the speed and reliability of the human’s output. We do this so much that eventually the whole factory is automated, and the human is simply observing. Nowhere in that story above is there a customer or factory worker feeding in open-ended inputs. The factory is precise, it takes inputs and produces outputs. The variability is restricted to variability of inputs and the reliability of the factory kit. Much business software is analogous to the factory. You have human workers who ultimately operate the business. And software is built to automate those tasks precisely. AI struggles because engineers are trying to build factories through incantation - if they just say the right series of magic spells, the LLM will produce a factory. And often it can. It’s just a shitty factory that does simple things, often inefficiently with unforeseen edge cases. At the moment, skilled factory builders (software engineers) are better at holistically understanding the needs of the business and building precise, maintainable, specific factories. The factory builders will use AI as a tool to help build better factories. Trying to get the AI to build the whole factory soup-to-nuts won’t work.
• killjoywashere 2y ago
  We have been looking at Hamming distance vs time to signature for ambient note generation in medicine. Any other metrics? Lots of metrics in the ML papers, but a lot of them seem sus. They take a lot of work to reproduce or they are designed around some strategy like maxing out the easy true negatives (so you get desirable accuracy and F1 score), etc. as someone trying to build validation protocols I can get vendors to enable (need them to write certain data from memory to a DB table we can access) I’d welcome that discussion. Right now the MBAs running the hospital systems are doing whatever their ML buddies say without regard to patient or provider.
• simonw 2y ago
  Yeah, the "book a flight" agent thing is a running joke now - it was a punchline in the Swyx keynote for the recent AI Engineer event in NYC: https://www.latent.space/p/agent https://www.latent.space/p/agent I think this piece is underestimating the difficulty involved here though. If only it was as easy as "just pick a single task and make the agent really good at that"! The problem is that if your UI involves human beings typing or talking to you in a human language, there is an unbounded set of ways things could go wrong. You can't test against every possible variant of what they might say. Humans are bad at clearly expressing things, but even worse is the challenge of ensuring they have a concrete, accurate mental model of what the software can and cannot do.
  • wdb 2y ago
    Can Google Flights find the best flight dates to a destination within a time frame? E.g. get flights to LA in a up to 15 day period with ensure attendance on 17 September. Fly with SkyAlliance airlines only. Flexible with any dates but needs to be there on 17 Sept and at minimum stay of eight days or more. Love if it could help with that but I haven't figured it out with Google Flights yet. My dream is to tell an AI agent the above and let it figure out the best deal.
  • burnte 2y ago
    > Yeah, the "book a flight" agent thing is a running joke now I literally sat in a meeting with one of our board members who used this exact example of how "AI can do everything now!" and it was REALLY hard not to laugh.
  • davesque 2y ago
    Yep, and AI agents essentially throw up a boundary blocking the user from understanding the capabilities of the system they're using. They're like the touch screens in cars that no one asked for, but for software.
  • photonthug 2y ago
    > The problem is that if your UI involves human beings typing or talking to you in a human language, there is an unbounded set of ways things could go wrong. You can't test against every possible variant of what they might say. It's almost like we really might benefit from using the advances in AI for stuff like speech recognition to build concrete interfaces with specific predefined vocabularies and a local-first UX. But stuff like that undermines a cloud-based service and a constantly changing interface and the opportunities for general spying and manufacturing "engagement" while people struggle to use the stuff you've made. And of course, producing actual specifications means that you would have to own bugs. Besides eliminating employees, much interest in AI is all about completely eliminating responsibility. As a user of ML-based monitoring products and such for years.. "intelligence" usually implies no real specifications, and no specifications implies no bugs, and no bugs implies rent-seeking behaviour without the burden of any actual responsibilities. It's frustrating to see how often even technologists buy the story that "users don't want/need concrete specifications" or that "users aren't smart enough to deal with concrete interfaces". It's a trick.
• ramesh31 2y ago
  More capability, less reliability please. I want something that can achieve superhuman results 1 out of 10 times, not something that gives mediocre human results 9 out of 10 times. All of reality is probabilistic. Expecting that to map deterministically to solving open ended complex problems is absurd. It's vectors all the way down.
  • soulofmischief 2y ago
    Stability is the bedrock of the evolution of stable systems. LLMs will not democratize software until an average person can get consistently decent and useful results without needing to be a senior engineer capable of a thorough audit.
    • ramesh31 2y ago
      >Stability is the bedrock of the evolution of stable systems. So we also thought with AI in general, and spent decades toiling on rules based systems. Until interpretability was thrown out the window and we just started letting deep learning algorithms run wild with endless compute, and looked at the actual results. This will be very similar.
      • skydhash 2y ago
        Rules based systems are quite useful, not for interacting with an untrained human, but for getting things done. Deep learning can be good at exploring the edges of a problem space, but when a solution is found, we can actually get to the doing part.
      • klabb3 2y ago
        This can be explained easily – there are simply some domains that were hard to model, and those are the ones where AI is outperforming humans. Natural language is the canonical example of this. Just because we focus on those domains now due to the recent advancements, doesn’t mean that AI will be better at every domain, especially the ones we understand exceptionally well. In fact, all evidence suggests that AI excels at some tasks and struggles with others. The null hypothesis should be that it continues to be the case, even as capability improves. Not all computation is the same.
• segh 2y ago
  Lots of people are building on the edge of current AI capabilities, where things don't quite work, because in 6 months when the AI labs release a more capable model, you will just be able to plug it in and have it work consistently.
  • techpineapple 2y ago
    In 6 months when FSD is completed, and we get robots in every home? I suspect we keep adding features, because reliability is hard. I do not know what heuristic you would be looking to conclude that this problem will eventually be solved by current AI paradigms.
    • thornewolf 2y ago
      GP comment is what has already happened "every 6 months" multiple times
  • postexitus 2y ago
    and where is that product that was developed on the edge of current AI capabilities and now with latest AI model plugged in it's suddenly working consistently? All I am seeing is models getting better and better in generating videos of spaghetti eating movie stars.
    • alltoowell 2y ago
      They're coming. I've seen the observability tools try to do this but I still have to tweak it. it's just time-consuming. Empromptu.ai is the closest to solving this problem. They are the only ones that have a library that you install in your to do system optimization, evals, for accuracy in real-time.
    • segh 2y ago
      For me, they have come from the AI labs themselves. I have been impressed with Claude Code and OpenAI's Deep Research.
      • 2y ago
• wiradikusuma 2y ago
  Booking a flight is actually task I cannot outsource to a human assistant, let alone AI. Maybe it's a third-world problem or just me being cheap, but there are heuristics involved when booking flights for a family trip or even just for myself. Check the official website, compare pricing with aggregator, check other dates, check people's availability on cheap dates. Sometimes I only do the first step if the official price is reasonable (I travel 1-2x a month, so I have expectation "how much it should cost"). Don't get me started if I also consider which credit card to use for the points rewards.
  • pton_xd 2y ago
    > Booking a flight is actually task I cannot outsource to a human assistant, let alone AI. Because there is no "correct" flight. Your preference changes as you discover information about what's available at a given time and price. The helpful AI assistant would present you with options, you'd choose what you prefer, it would refine the options, and so on, until you make your final selection. There would be no communication lag as there would be with a human assistant. That sounds very doable to me.
  • amogul 2y ago
    I feel the same way, or at least I wouldn't delegate this unless they fine tune accuracy and reliability in their apps. Right now, it sits around 40-60%
  • csomar 2y ago
    It is not that you can't outsource it, but there are so many variables that once you finished explaining them to the assistant (human or AI) you'd be better off doing it yourself. A human assistant only makes sense if he/she is your everything assistant and has knowledge about your work schedule, life, kids, financials, etc...
  • baxtr 2y ago
    There is a really interesting book called Alchemy by Rory Sutherland. In one chapter he describes his frustration with GPS based navigation apps. I thought it was similar to what you describe. > If I am commuting home, I may prefer a slower route that avoids traffic jams. (Humans, unlike GPS devices, would rather keep moving slowly than get stuck in stop-start traffic.) GPS devices also have no notion of trade-offs, in particular relating to optimising ‘average, expected journey time’ and minimising ‘variance’ – the difference between the best and the worst journey time for a given route. For instance, whenever I drive to the airport, I often ignore my GPS. This is because what I need when I’m catching a flight is not the fastest average journey, but the one with the lowest variance in journey time – the one with the ‘least-bad worst-case scenario’. The satnav always recommends that I travel there by motorway, whereas I mostly use the back roads.
• qoez 2y ago
  You get more reliability from better capability though. More capability means being better at not misclassifying subtle tasks, which is what causes reliability issues.
• joshdavham 2y ago
  My rule of thumb has thus far been: if I’m gonna allow AI to write any bit of code for me, then I must, at a bare minimum, be able to understand that code. There’s no way I could do what some of these “vibe coders” are doing where they allow AI to write code for them that they don’t even understand.
  • kevmo314 2y ago
    That's only true as long as you want to modify said code. If it meets your bar for reliability then you won't need to understand it, much like how we don't really need to read/understand compiled assembly code so we largely trust the compiler. A lot of these vibe coders just have a much lower bar for reliability than you.
    • fourside 2y ago
      How do you know if it meets your bar for reliability if you don’t understand the output? I don’t know that the analogy to a compiler is apples to apples. A compiler isn’t producing an answer based on statistically generating something that should look like the right answer.
      • kevmo314 2y ago
        The premise for vibe coding is that it's generating the entire app or site. If the app does what you want then it's meeting the bar.
    • joshdavham 2y ago
      This is an interesting point and it's certainly true with respect to most peoples' attitudes towards dependencies. For example, while I feel the need to understand the code I wrote using pytorch, I don't generally feel the need to totally grok how pytorch works.
  • AlexandrB 2y ago
    I think there's a lot of code that gets written that's either disposable or effectively "write only" in that no one is expected to maintain it. I have friends who write a lot of this code for tasks like data analysis for retail and "vibe coding" isn't that crazy in such a domain. Basically, what's worse? "Vibes" code that no one understands or a cascade of 20 spreadsheets that no one understands? At least with the "vibes" code you can stick it in git and have some semblance of sane revision control and change tracking.
• mentalgear 2y ago
  Capability demos (like Rabbit R1 vaporware) will go up as long as the market is hot and investors (like lemmings) foolishly running after those companies that are best @ hype.
• deleted 2y ago
  [deleted]
• marban 2y ago
  Giving up accuracy for a bit of convenience—if any at all—almost never pays off. Looking at you, Alexa.
  • danielbln 2y ago
    Image compression, eventual consistency, fuzzy search. There are many more examples I'm sure.
    • skydhash 2y ago
      > Image compression, eventual consistency, fuzzy search. There are many more examples I'm sure. Isn't all of these very deterministic? You can predict what's going to be discarded by the compression algorithm. Eventual consistency is only eventual because of the generation of events. Once that stops, you will have a consistent system and the whole thing can be replayed based on the history of events. Even with fuzzy search you can intuit how to get reliable results and ordering without even looking at the algorithms. An LLMs based agent is the least efficient method for most of the cases they're marketing if for. Sometimes all you need is a rule-based engine. Then you can add bounded fuzziness where it's actually helpful.
• bhu8 2y ago
  I have been thinking about the exact same problem for a while and was literally hours away from publishing a blogpost on the subject. +100 on the footnote: > agents or workflows? Workflows. Workflows, all the way. The agents can start using these workflows once they are actually ready to execute stuff with high precision. And, by then we would have figured out how to create effective, accurate and easily diagnozable workflows, so people will stop complaining about "I want to know what's going on inside the black box".
  • DebtDeflation 2y ago
    I've been building workflows with "AI" capability inserted where appropriate since 2016. Mostly customer service chatbots. 99.9% of real world enterprise AI use cases today are for workflows not agents. However, "agents" are being pushed because the industry needs a next big thing to keep the investment funding flowing in. The problem is that even the best reasoning models available today don't have the actual reasoning and planning capability needed to build truly autonomous agents. They might in a year. Or they might not.
  • breckenedge 2y ago
    Agreed, I started crafting workflows last week. Still not impressed with how poorly the current crop of models is at following instructions. And are there any guidelines on how to manage workflows for a project or set of projects? I’m just keeping them in plain text and including them in conversations ad hoc.
• narmiouh 2y ago
  I feel like OP would have been better of not referencing the viral thread about a developer not using any version control and surprised when the AI made changes, I don't think anyone who doesn't understand version control should be using a tool like cursor, there are other SAAS apps that build and deploy apps using AI and for people with the skill demonstrated in the thread, that is what they should be using. It's like saying rm -rf / should have more safeguards built in. It feels unfair to call out the AI based tools for this.
  • empath75 2y ago
    I actually had cursor on in yolo mode with the github integration on and it's actually pretty good about doing commits and pushes and opening PRs and stuff. Though I would never do that in a repo I cared a lot about. It doesn't always know what branch it's on and makes assumptions all the time about what org it's in, etc...
  • fabianhjr 2y ago
    `rm -rf /` does have a safeguard: > For example, if a user with appropriate privileges mistakenly runs ‘rm -rf / tmp/junk’, that may remove all files on the entire system. Since there are so few legitimate uses for such a command, GNU rm normally declines to operate on any directory that resolves to /. If you really want to try to remove all the files on your system, you can use the --no-preserve-root option, but the default behavior, specified by the --preserve-root option, is safer for most purposes. https://www.gnu.org/software/coreutils/manual/html_node/Treating-_002f-specially.html https://www.gnu.org/software/coreutils/manual/html_node/Trea...
    • layer8 2y ago
      That was added in 2006, so didn’t exist for a good half of its life (even longer if you count pre-GNU). I remember rm -rf / being considered just one instance of having to double-check what you do when using the -rf option. It’s one reason it became common to alias rm to rm -i.
  • danso 2y ago
    I think it's a useful anecdote because it underscores how catastrophically unreliable* agents can be, especially in the hands of users who aren't experienced in the particular domain. In the domain of programming, it's much easier to quantify a "catastrophic" scenario vs. more open-ended "real world" situations like booking a flight. * "unreliable" may not be the right word. For all we know, the agent performed admirably given whatever the user's prompt may have been. Just goes to show that even in a relatively constricted domain of programming, where a lot (but far from all) outcomes are binary, the room for misinterpretation and error is still quite vast.
• daxfohl 2y ago
  We can barely make deterministic distributed services reliable. And microservices now have a bad reputation for being expensive distributed spaghetti. I'm not holding my breath for distributed AI agents to be a thing.
• jappwilson 2y ago
  Can't wait for this being a plot point in a murder mystery, someone gamed the AI agent to create a planned "accident"
• twotwotwo 2y ago
  FWIW, work has pushed use of Cursor and I quickly came around to a related conclusion: given a reliability vs. anything tradeoff, you more or less always have to prefer reliability. For example, even ignoring subtle head-scratcher type bugs, a faster model's output on average needs more revision before it basically works, and on average you end up spending more energy on that than you save by reducing time to first response. Up-front work that decreases the chance of trouble--detailing how you want something done, explicitly pulling into context specific libraries--also tends to be worth it on net, even if the agent might have gotten there by searching (or you could get it there through follow-up requests). That's my experience working with a largeish mature codebase (all on non-prod code) where you can't get far if you can't use various internal libraries correctly. With standalone (or small greenfield) projects, where results can lean more on public info from pre-training and there's not as much project specific info to pull in, you might see different outcomes. Maybe the tech and surrounding practice will change over time, but in my short experience it's mostly been about trying to just get to 'acceptable' for this kind of task.
• asdev 2y ago
  want reliability? build automation instead of using non deterministic models to complete tasks
• nottorp 2y ago
  But but... People don't get promoted for reliability. They get promoted for new capabilities. Everyone thinks they're the next Google.
• xg15 2y ago
  > If your task can be expressed as a workflow, build a workflow. And miss out on the sweet, sweet VC millions? Naah.
• prng2021 2y ago
  I think the best shot we have at solving this problem is an explosion of specialized agents. That will limit how off the rails each one can go at interpreting or performing some type of task. The end user still just needs to interact with one agent though, as long as it can delegate properly to subagents.
• SkyPuncher 2y ago
  Unfortunately, the picked example kind of weighs down the point. Cursor has an extremely vocal minority (beginner coders) that isn't really representative of their heavy weight users (professional coders). These beginner users face significant issues that come from being new to programming, in general. Cursor gives them amazing capabilities, but it also lets them make the same dumb mistakes that most professional developers have done once or twice in their career. That being said, back in February I was trying out of bunch of AI personal assistant apps/tools. I found, without fail, every single one of them was advertising features their LLMs could theoretically accomplish, but in practice couldn't. Even worse was many of these "assistants" would proactively suggest they could accomplish something but when you sent them out to do it, they'd tell you they couldn't. * "Would you like me to call that restaurant?"...."Sorry, I don't have support for that yet" * "Would you like me to create a reminder?"....Created the reminder, but never executed it * "Do you want me to check their website?"...."Sorry, I don't support that yet" Of all of the promised features, the only thing I ended up using any of them for was a text message interface to an LLM. Now that Siri has native ChatGPT support, it's not necessary.
• _cs2017_ 2y ago
  Does anyone have AI agent use cases that that you think might happen within this year and that feels very exciting to you? I personally struggle to find a new one (AI agent coding assistants already exist, and of course I'm excited about them, especially as they get better). I will not, any time soon, trust unsupervised AI to send emails on my behalf, make travel reservations, or perform other actions that are very costly to fix. AI as a shopping agent just isn't too exciting for me, since I do not believe I actually know what features in a speaker / laptop / car I want until I do my own research by reading what experts and users say.
• danso 2y ago
  I think the replies [0] to the mentioned reddit thread sums up my (perhaps complacent?) feelings about the current state of automated AI programming: > Does it terrify anyone else that there is an entire cohort of new engineers who are getting into programming because of AI, but missing these absolute basic bare necessities? > > Terrify? No, it's reassuring that I might still have a place in the world. [0] https://www.reddit.com/r/cursor/comments/1inoryp/comment/mdoksrj/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button https://www.reddit.com/r/cursor/comments/1inoryp/comment/mdo...
  • bob1029 2y ago
    The reddit post feels like engagement bait to me. Why would you ask the community a question like "how to source control" when you've been working with (presumably) a programming genius LLM that could provide the most personally tailored path for baby's first git experience? Even if you don't know that "git" is a thing, you could ask questions as if you were a golden retriever and the model would still inevitably recommend git in the first turn of conversation. Is it really the case that a person who has the ability to use a compiler, IDE, LLM, web browser, reddit, etc., somehow simultaneously lacks the ability to frame basic-ass questions about the very mission they set out on? If stuff like this is not manufactured, then we should all walk away feeling pretty fantastic about our future job prospects.
    • danso 2y ago
      The account is a throwaway but based on its short posting history and its replies, I don't have reason to believe it's a troll: https://www.reddit.com/r/cursor/comments/1inoryp/comment/mdrps9b/ https://www.reddit.com/r/cursor/comments/1inoryp/comment/mdr... > I'm not a dev or engineers at all (just a geek working in Finance) This fits my experience of teaching very intelligent students how to code; if you're an experienced programmer, you simply cannot fathom the kinds of assumptions beginners will make due to gaps in yet-to-be foundational knowledge. I remember having to tell students to mindful when searching Stack Overflow for help, because of how something as simple as an error from Requests (e.g. while doing web scraping) could lead them down a rabbit hole of "solutions" such as completely uninstalling their Python for a different/older version of Python.
• donfotto 2y ago
  > choosing a small number of tasks to execute exceptionally well And that is the Unix philosophy
• vivzkestrel 2y ago
  remember 2016 chatbots anymore. sounds like the same thing all over again except this time we got hallucinations and unpredictability
• hirako2000 2y ago
  The problem with Devin wasn't that it was a black box doing too much. It's that the outcome demo'd were fake and what was inside the box wasn't an "AI engineer." Transparency? If it worked even unreliably, nobody would care what it does. Problem is stochastic machines aren't engineers, don't reason, are not intelligence. I find articles attacking Ai but finding excuses in some mouse rather than pointing at the elephant, exhausting.
• mosura 2y ago
  [dead]
• ankit219 2y ago
  Agents in the current format are unlikely to go beyond a current levels of reliability. I believe agents are a good use case in a low trust environments (outside of coding where you could see the errors quickly with testing or deployment) like inter-company communications and tasks, where there are already systems in place for checks and things going wrong. Might be a hot space in some time. For intra company, high trust environment cannot just be a workflow automation given any error would need the knowledge worker to redo the whole thing to check if its correct. We can do it via other agents - less chances of it going wrong - but more chances it screws up in the same place as previous one.
• shireboy 2y ago
  " It’s easy to blame the user's missing grasp of basic version control, but that misses the deeper point." Uhh, no, that's pretty much the point. A developer without basic understanding of version control is like a pilot without a basic understanding of landing. A ton of problems with AI (or any other tool, including your own brain) get fixed by iterating on small commits and branching. Throw away the commit or branch if it really goes sideways. I can't fathom working on something for 4 months without realizing a problem or having any way to roll back. That said, the one argument I could see is if Cursor (or copilot, etc) had built in to suggest "this project isn't in source control, we should probably do that before getting too far ahead of ourselves.", then help the user setup sc, repo, commit, etc. The topic _is_ tricky and I do remember not totally grasping git, branching, etc.
  • highmastdon 2y ago
    The nice thing is that adding this to the basic prompt that cursor uses will advance all those users and directly do away with this problem only to discover the next one. However, all these little things add up to a very powerful prompt where the LLM will make it only easier for anyone to build real stuff that on the surface looks very good
• andreash 2y ago
  We are building this with https://lets.dev https://lets.dev. We believe there will be great demand for less capable, but much more determinisic agents. I also recommend everyone to read "What is an agent?" by Harrison Chase. https://blog.langchain.dev/what-is-an-agent/ https://blog.langchain.dev/what-is-an-agent/
  • mdaniel 2y ago
    That's some pretty big chutzpah putting up an 3l33t page and a "subscribe to our mailing list" input box to draw in potential customers But hey, this whole thread is about the warring factions about whether anything matters anymore, so don't listen to me
• tristor 2y ago
  The thing I most want an AI agent to do is something I can't trust to any third-party, it'd need to be local, and it's something well within LLM capabilities today. I just want a "secretary in my pocket" to take notes during conversations and produce minutes, but do so in a way that's secure and privacy-respecting (e.g. I can use it at work or at home).
  • htrp 2y ago
    get a pixel/s24 with on device asr & summaries
• anishpalakurT 2y ago
  Check out BAML at boundaryml.com
  • alltoowell 2y ago
    baml isn't great. You need to write it in their format and it still doesnt really solve the accuracy problem from humans interacting with your system that we're talking about here.
• piokoch 2y ago
  Funny note about Cursor. Commercial project, rather expensive, cannot figure out that it would be good to use, say, version control not to break somebody's work. That's why I prefer Aider (free), which is simply committing whatever it does, so any change could be reverted. Easily.
• jlaneve 2y ago
  I appreciate the distinction between agents and workflows - this seems to be commonly overlooked and in my opinion helps ground people in reliability vs capability. Today (and in the near future) there's not going to be "one agent to rule them all", so these LLM workflows don't need to be incredibly capable. They just need to do what they're intended to do _reliably_ and nothing more. I've started taking a very data engineering-centric approach to the problem where you treat an LLM as an API call as you would any other tool in a pipeline, and it's crazy (or maybe not so crazy) what LLM workflows are capable of doing, all with increased reliability. So much so that I've tried to package my thoughts / opinions up into an AI SDK for Apache Airflow [1] (one of the more popular orchestration tools that data engineers use). This feels like the right approach and in our customer base / community, it also maps perfectly to the organizations that have been most successful. The number of times I've seen companies stand up an AI team without really understanding _what problem they want to solve_... [1] https://github.com/astronomer/airflow-ai-sdk https://github.com/astronomer/airflow-ai-sdk
• LeifCarrotson 2y ago
  Unfortunately, LLMs, natural language, and human cognition largely are what they are. Mix the three together and you don't get reliability as a result. It's not like there's a lever in Cursor HQ where one side is "Capability" and one side is "Reliability", and they can make things better just by tipping it back towards the latter. You can bias designs and efforts in that direction, and get your tool to output reversible steps or bake in sanity checks to blessed actions, but that doesn't change the nature of the problem.
• rambambram 2y ago
  I heard you, so we decided to now tweak the dials a bit. The dial for 'capability' we can turn back a little, no problem, but the dial for 'reliability', uhm yeah... I'm sorry, but we couldn't find that dial. Sorry.
• extr 2y ago
  The problem I find in many cases is that people are restrained by their imagination of what's possible, so they target existing workflows for AI. But existing workflows exist for a reason: someone already wanted to do that, and there have been countless man-hours put into the optimization of the UX/UI. And by definition they were possible before AI, so using AI for them is a bit of a solution in search of a problem. Flights are a good example but I often cite Uber as a good one too. Nobody wants to tell their assistant to book them an Uber - the UX/UI is so streamlined and easy, it's almost always easy enough to just do it yourself (or if you are too important for that, you probably have a private driver already). Basically anything you can do with an iPhone and the top 20 apps is in this category. You are literally competing against hundreds of engineers/product designers who had no other goal than to build the best possible experience for accomplishing X. Even if LLMs would have been helpful a priori - they aren't after every edge case has already been enumerated and planned for.
  • arionhardison 2y ago
    > The problem I find in many cases is that people are restrained by their imagination of what's possible, so they target existing workflows for AI. I concur and would like to add that they are also restrained by the limitations of existing "systems" and our implicit and explicit expectations of said system. I am currently attempting to mitigate the harm done by this restriction by focusing on and starting with a first principal analysis of the problem being solved before starting the work, for example; lets take a well established and well documented system like the SSA. When attempting to develop, refactor, extend etc... such a system; what is the proper thought process. As I see it, there are two paths: Path 1: a) Breakdown the existing workflows b) Identify key performance indicators (KPIs) that align with your business goals c) Collect and analyze data related to those KPIs using BPM tools d) Find the most expensive worst performing workflows e) Automate them E2E w/ interface contracts on either side This approach locks you into to existing restrictions of the system, workflows, implementation etc... Path 2: a) Analyze system to understand goal in terms of 1st principals, e.g: What is the mission of the SSA? To move money based on conditional logic. b) What systems / data structures are closest to this function and does the legacy system reflect this at its core e.g.: SSA should just be a ledger IMO c) If Yes, go to "Path 1" and if No go to "D" d) Identify the core function of the system, the critical path (core workflow) and all required parties e) Make MVP which only does the bare min By following path 2 and starting off with an AI analysis of the actual problem and not the problem as it exist as a solution within the context of an existing system, it is my opinion that the previous restrictions have been avoided. Note: Obviously this is a gross oversimplification of the project management process and there are usually external factors that weigh in and decide which path is possible for a given initiative, my goal here was just to highlight a specific deviation from my normal process that has yielded benefits so far in my own personal experience.
• peterjliu 2y ago
  We've (ex Google Deepmind researchers) been doing research in increasing the reliability of agents and realized it is pretty non-trivial, but there are a lot of techniques to improve it. The most important thing is doing rigorous evals that are representative of what your users do in your product. Often this is not the same as academic benchmarks. We made our own benchmarks to measure progress. Plug: We just posted a demo of our agent doing sophisticated reasoning over a huge dataset ((JFK assassination files -- 80,000 PDF pages): https://x.com/peterjliu/status/1906711224261464320 https://x.com/peterjliu/status/1906711224261464320 Even on small amounts of files, I think there's quite a palpable difference in reliability/accuracy vs the big AI players.
  • ai-christianson 2y ago
    > The most important thing is doing rigorous evals that are representative of what your users do in your product. Often this is not the same as academic benchmarks. OMFG thank you for saying this. As a core contributor to RA.Aid, optimizing it for SWE-bench seems like it would actively go against perf on real-world tasks. RA.Aid came about in the first place as a pragmatic programming tool (I created it while making another software startup, Fictie.) It works well because it was literally made and tested by making other software, and these days it mostly creates its own code. Do you have any tips or suggestions on how to do more formalized evals, but on tasks that resemble real world tasks?
    • peterjliu 2y ago
      I would start by making the examples yourself initially, assuming you have a good sense for what that real-world task is. If you can't articulate what a good task is and what a good output is, it is not ready for out-sourcing to crowd-workers. And before going to crowd-workers (maybe you can skip them entirely) try LLMs.
      • ai-christianson 2y ago
        > I would start by making the examples yourself initially What I'm doing right now is this: 1) I have X problem to solve using the coding agent. 2) I ask the agent to do X 3) I use my own brain: did the agent do it correctly? If the agent did not do it correctly, I then ask: should the agent have been able to solve this? If so, I try to improve the agent so it's able to do that. The hardest part about automating this is #3 above --each evaluation is one-off and it would be hard to even formalize the evaluation. SWE bench, for example uses unit tests for this, and the agent is blind to the unit tests --so the agent has to make a red test (which it has never seen) go green.
• jedberg 2y ago
  I've been working on this problem for a while. There are whole companies that do this. They all work by having a human review a sample of the results and score them (with various uses of magic to make that more efficient). And then suggest changes to make it more accurate in the future. The best companies can get up to 90% accuracy. Most are closer to 80%. But it's important to remember, we're expecting perfection here. But think about this: Have you ever asked someone to book a flight for you? How did it go? At least in my experience, there's usually a few back and forth emails, and then something is always not quite right or as good as if you did it yourself, but you're ok with that because it saved you time. The one thing that makes it better is if the same person does it for you a couple of times and learned your specific habits and what you care about. I think the biggest problem in AI accuracy is expecting the AI to be better than a human.
  • morsecodist 2y ago
    This is really cool. I agree with your point that a human would also struggle to book a flight for someone but what I take from that is conversation is not the best interface for picking flights. I am not really sure how you beat a list of available flights + filters. There are a lot of criteria: total fight time, price, number of stops, length of layover, airline, which airport if your destination is served by multiple airports. I couldn't really communicate to anyone how I weigh those and it shifts over time.
  • lolinder 2y ago
    > I think the biggest problem in AI accuracy is expecting the AI to be better than a human. If it's not better across at least one of {more accurate, faster, cheaper} then there is no business. You have to be offering one of the above. And that applies both to humans and to existing tech solutions: an LLM solution must beat both in some dimension. Current flight booking interfaces are actually better than a human at all three: they're more accurate, they're free, and they're faster than trying to do the back and forth, which means the bar to clear for an agent is extremely high.
    • bluGill 2y ago
• fennecbutt 2y ago
  Lmao, training models off what is essentially a process directly inspired by imperfect "Good enough" biological processes and expecting it to be a calculator. Ofc I'm not defending all thy hype and I look forward to more advanced models that get it right more often. But I do laugh at him tech people and managers who expect ml based on an analog process to be sterile and clean like a digital environs.
• Havoc 2y ago
  What has me slightly puzzled is why there isn’t a sharp pivot towards typed languages for vibe coding. Would be much easier for the AI/IDE to confirm the code is likely good. Or well better than untyped. The whole rust if it compiles it probably works thing. Instead it’s all python/JS let LLM write code and pray you don’t hit run time errors on a novel code path I get that there is more python training data but still seems like the inferior fit for LLM assisted coding
• cnst 2y ago
  This is my biggest complaint about AI. Instead of creating easy-to-navigate help sections of the website, and explaining the product and everything clearly, the flashy vendors simply put everything behind an opaque model as if that's somehow better. Then you have to guess what to type to get the most basic info about fees, terms and procedures of a service. You want to see how the Pros are doing it? Well, they're not using any AI! Tesla, for example, still has a regular PDF and a regular section-based manual (in HTML) where you can read the details about your car. $TSLA is priced as being the most innovative auto manufacturer, and they're clearly proficient with the AI (Autopilot/FSD), yet when it comes to user's manual, clearly they're following the same process as all the legacy automakers always have had (besides not hiding the PDF behind a parts paywall, and having an open-access HTML version of the manual, too, of course). Why? Because that actually works!