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

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, but I can easily see this happening.

Some recent actual uses cases for me where an agent would NOT be able to help me although I really wish it would:

1. An agent to automate generating web pages from design images - Given an image, produce the HTML and CSS. LLMs couldn't do this for my simple page from a web designer. Not even close, even mixing up vertical/horizontal flex arrangement. When I cropped the image to just a small section, it still couldn't do it. Tried a couple LLMs, none even came close. And these are pretty simple basic designs! I had to do it all manually.

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.

3. Illustrator Agent - Image generation for above story. Images end up very "LLM" looking, often miss key elements in the story, but one thing is worst of all: no persistent characters. This is already a big problem with text, but an even bigger problems with images. Every image for the same story has a character who looks different, but I want them to be the same.

4. Publisher Agent - Package things together above so I can get a complete package of illustrated stories on topics available on web/mobile for viewing, tracking progress, at varying levels.

Just some examples of where LLMs are currently not moving the needle much if at all.

I keep asking the "experts" on Linkedin all the time, show me real life uses - radio silence.

We're using AI agents for the orchestration of our fully automated web scrapers. But instead of trying to have one large general purpose agent that is hard to control and test, we use many smaller agents that basically just pick the right strategy for a specific sub-task in our workflows. In our case, an agent is a medium-sized LLM prompt that has a) context and b) a set of functions available to call.

For example we use it for:

- Website Loading: Automate proxy and browser selection to load sites effectively. Start with the cheapest and simplest way of extracting data, which is fetching the site without any JS or actual browser. If that doesn't work, the agent tries to load the site with a browser and a simple proxy, and so on.

- Navigation: Detect navigation elements and handle actions like pagination or infinite scroll automatically.

- Network Analysis: Identify desired data within network calls.

- Validation: Hallucination checks and verification that the data is actually on the website and in the right format. (this is mostly traditional code though)

- Data transformation: Clean and map the data into the desired format. Finetuned small and performant LLMs are great at this task with a high reliability.

The main challenge:

We quickly realized that doing this for a few data sources with low complexity is one thing, doing it for thousands of websites in a reliable, scalable, and cost-efficient way is a whole different beast.

The integration of tightly constrained agents with traditional engineering methods effectively solved this issue for us.

Edit: You can try out a simplified version of this in our playground: https://www.kadoa.com/add

They're already very good at pissing off your customers in the "support" section of your website.

Don't downplay the value of watching agents talk to each other for amusement. I got a lot of mileage out of that and will continue to do so.