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Ask HN: What are some actual use cases of AI Agents right now?

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Re: Ask HN: What are some actual use cases of AI Agents right now?

#81
Almost all the AI Apps we build for our clients now use Autonomous Assistants.

They're simply better than naive RAG, especially when you need to access APIs, format content or compare different sections of the knowledge base.

Here are a few demos we have in the open:

> HackerNews AI: Interacts with the hackernews API - https://hn.aidev.run

> ArXiv AI: Reads, summarizes and compares arxiv papers - https://arxiv.aidev.run

(love that it can give you a comparison between 2 papers)

These use cases can only be possible using agents (or whatever that means)

Re: Ask HN: What are some actual use cases of AI Agents right now?

#82
post #40

> I'd love to know what are some of the hidden challenges to making a useful product with agents? One thing that is still confusing to me, is that we've been building products with machine learning pretty heavily for a decade now and somehow abandoned all that we have learned about the process now that we're building "AI". The biggest thing any ML practitioner realizes when they step out of a research setting is that…

> You can do handwritten digit recognition with 90% accuracy? Sounds pretty good, but if you need to turn that into recognizing a 12 digit account number you now have a 70% chance of getting at least one digit incorrect. You are assuming that the probability of failure is independent, which couldn't be further from the truth. If a digit recogniser can recognise one of your "hard" handwritten digits, such as a 4 or a…

And the US Post Office and other postal services have been using this tech to sort letters for several decades now (although postal codes with both letters and numbers like Canada's are harder). It was viewed as the "killer app" for ML in the 1990s.

Re: Ask HN: What are some actual use cases of AI Agents right now?

#83

> I'd love to know what are some of the hidden challenges to making a useful product with agents? One thing that is still confusing to me, is that we've been building products with machine learning pretty heavily for a decade now and somehow abandoned all that we have learned about the process now that we're building "AI". The biggest thing any ML practitioner realizes when they step out of a research setting is that…

The first think any ML practictioner realizes is that accuracy is about the single worst performance metric you can use for most real-world tasks, lol

Re: Ask HN: What are some actual use cases of AI Agents right now?

#84
post #77

Earlier quoted context omitted.

Yea that's a good point. Now I am curious, what are some tasks that can accept a model that is at 80% as good as a human, but is 100x cheaper?(or, 100x faster?)

Similar to the sibling comment, helpdesk ticket routing. The volume of helpdesk tickets large enterprises deal with is very easily and vastly underestimated. If you can even route 30% away from the central triage with 90+% accuracy and drop everything else back to the central triage... you suddenly safe 2 FTEs in that spot in some places. And increase customer satisfaction for most of those tickets because they get r…

Lemon

Re: Ask HN: What are some actual use cases of AI Agents right now?

#85

> I'd love to know what are some of the hidden challenges to making a useful product with agents? One thing that is still confusing to me, is that we've been building products with machine learning pretty heavily for a decade now and somehow abandoned all that we have learned about the process now that we're building "AI". The biggest thing any ML practitioner realizes when they step out of a research setting is that…

> The biggest thing any ML practitioner realizes when they step out of a research setting is that for most tasks accuracy has to be very high for it be productizable. I think that ChatGPT's success might be partly attributable to its chat interface. For whatever reason, a lot of people - including me! - are much more forgiving of inconsistencies, slip-ups, and inaccuracies when in a conversational format. Kind of lik…

There are several reasons to forget:

  - copilots are useful
  - chat is entertaining and useful
  - future tech is coming
  - investment money

Re: Ask HN: What are some actual use cases of AI Agents right now?

#86

Earlier quoted context omitted.

A ton of tasks. Call centers to start with (they already do[1]), with human fallback. 1: In my country, after ChatGPT launched last year, when you call customer support you are now prompted to “just say in a few words” what you want instead of going through tap-this-number menus (they exist as a fallback) and I believe the backend is an LLM. The user flow and voice recordings are still programmatically determined tho…

I speak English clearly. These things always tell me to repeat what I said. Never once has it ever worked for me. I want to throw my phone at the wall. Also I think this has been around for longer than chatgpt. It is often accompanied by a fake keyboard clicking noise.

> I speak English clearly. These things always tell me to repeat what I said. Never once has it ever worked for me. I want to throw my phone at the wall

Now imagine how well it works for people with non-"native" accents (even for native, I'd guess that a good Scouse/Glaswegian/Kiwi accent might confuse the hell of those systems as well). It's a disaster and I hate those.

Re: Ask HN: What are some actual use cases of AI Agents right now?

#87

My RSS reader is an A.I. agent, I have written a huge number of comments mentioning it https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...

That’s not exactly taking actions on your behalf though. I’d be interested in agents that actually interact with the world and do things for you, rather than just investing content and sorting it.

This 2007 book reveals the method of getting value out of cognitive systems

https://www.thriftbooks.com/w/smart-enough-systems-how-to-de...

Note I can hit a button on a link and prepare a post for Hacker News which goes into a queue that drains about as fast as I think I can get away with. I could easily have the model schedule top-scoring posts on metrics like "likely to have a knock-down-drag-out discussion" but I think that would be wrong. It is a feature not a bug that YOShInOn requires my assent in that I can enforce my own values and because I work closely with it, it learns certain aspects of those values.

YOShInOn Enterprise Edition would have a plurality of classification and generative models connected with the user interface for that co-working with the plan that the system processes asynchronous workflows (e.g. "generate a series of blog posts", "respond to customer requests") where some of the steps are automated and some are manual and the long-term goal is to reduce the manual, in the short term you are going to be making a lot of labels.

Re: Ask HN: What are some actual use cases of AI Agents right now?

#88

None of these I've seen actually works in practice. Having used LLMs for software development the past year or so, even the latest GPT-4/Gemini doesn't produce anything I can drop in and have it work. I've got to go back and forth with the LLM to get anything useful and even then have to substantially modify it. I really hope there are some big advancements soon and this doesn't just collapse into another AI winter,…

> 2. Story Generator Agent - Write a story from a given outline (for educational purposes). Even at a very detailed outline level, and with a large context window, kept forgetting key points, repetitive language, no plot development. I just have to write the story myself.

You have to break it down into smaller steps and provide way more detail than you think you do in the context. I did an experiment in story generation where I had "authors" that would write only from the perspective of one of the characters that was also completely generated starting first from genre, name, character traits, etc. Then for a given scene, within a given plot and where in the story you are, randomly rotate between authors for each generation, appending it in memory, but not all of the story fits in context. And each generation is only a couple hundred tokens where you ask it to start/continue/end the story. The context contains all of this information in a simple key:value format. And essentially treat the LLM like a loom and spin the story out.

Usually what it produces isn't quite the best, but that's okay, because you can further refine the generation by using different system/user prompts explicitly for editing the content. I found that asking it to suggest one refinement and phrase it as a direct command, then feeding that command with the original generation, works. This meta-prompting tends to produce changes that subjectively improve the text according to whatever dimensions specified in the system prompt.

If you treat the composition as way more mechanical with tightly constrained generation, you get a much better, much more controlled result.

Re: Ask HN: What are some actual use cases of AI Agents right now?

#89

Earlier quoted context omitted.

I am confused to where this leaves us. Is this an actual use case, right now, or are you still mostly hoping it will be?

We're actively using this approach at scale, although still improving :) You can try out a simplified version of this in our playground: https://www.kadoa.com/add

I hadn’t heard of Kadoa before. Thanks for sharing it sounds like an interesting problem to solve.

Re: Ask HN: What are some actual use cases of AI Agents right now?

#90

> I'd love to know what are some of the hidden challenges to making a useful product with agents? One thing that is still confusing to me, is that we've been building products with machine learning pretty heavily for a decade now and somehow abandoned all that we have learned about the process now that we're building "AI". The biggest thing any ML practitioner realizes when they step out of a research setting is that…

> The biggest thing any ML practitioner realizes when they step out of a research setting is that for most tasks accuracy has to be very high for it be productizable. I think that ChatGPT's success might be partly attributable to its chat interface. For whatever reason, a lot of people - including me! - are much more forgiving of inconsistencies, slip-ups, and inaccuracies when in a conversational format. Kind of lik…

I don't know if I'm more forgiving of inaccuracies in a conversational interface, but I'm way less likely to notice them in the first place. Especially since the current crop of RLHF'd models are so eager to please that they say nearly everything with high confidence.
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