Are there any real examples of AI agents doing work?

Been in BigCo land for 20 years now, and have seen the rise and fall of quite a few AI/ML/RPA etc fads.

Honestly the whole landscape seems broken and unproductive at this point.

Countless vendors, platforms, cloud environments, industry/technical jargon - all with different pricing models, SLAs, tooling, etc etc.

Getting anything usable is a challenge and most orgs spin in a never ending cycle of data integration/normalization work that produces little business value.

My advice to teams now is simplify, reduce, streamline - get to the kernel of what you think you need and protect it all costs. Most of the shiny new objects being pitched as silver bullets are just ways for other people to make money off your margin.

I too would like to hear some examples.

On the one hand you have gurus claiming that AI agents are going to all make all SaaS redundant, on the other claiming that AI isn't going to take my coding job, but I need to adapt my workflows to incorporate AI. We all need to start preparing now for the changes that AI is going to cause.

But these two claims aren't compatible. If AGI and these super agents are that bonkers amazeballs that they can replace entire SaaS companies - then there is no way I'm going to be able to adapt my workflows to compete as a programmer.

Further, if the wildest claims about AI end up proving to be true - there is simply no way to prepare. What possible adaptation to my workflow could I possibly come up with that an AI agent could not surpass? Why should I bother learning how to implement (with today's apis) some RAG setup for a SaaS customer service chatbot when presumably an AI agent is going to make that skillset redundant shortly after?

I'm going to be interviewing for frontend roles soon, and for my prep I'm just going back to basics and making sure I remember on demand all the basics css, html, js/ts - fuck the rest of this noise.

I like this distinction from automation by Bartosz Pucek:

At its core, an Agent is software that can:

    Take in a task description

    Break it down into steps

    Execute those steps using available tools

    Adapt its approach based on feedback

The key distinction from traditional automation: Agents handle variance and uncertainty by replanning rather than failing when their happy path breaks.

Source: https://newsletter.pucek.com/p/2025-the-state-of-ai-agents-a...

We are working on a project Potpie (https://github.com/potpie-ai/potpie). It's basically an open-source AI agentic platform that helps the developers to build AI Agents that truly understand your complex codebases and performed desired actions. Unlike majority of the AI Agent platforms and GenAI models, Potpie's AI Agents can understand the overall context of your codebase thoroughly

Amazing that at nearing the 50 comment mark and there only seems to be people who have successfully created tutorial examples? And some other things that could be done with more purpose specific traditional solutions. And some people showing love for the concepts. This is probably the bleakest I've seen a Ask HN thread considering this is where all the money is going. I think one stark thing that maybe isn't being addressed is that the value of the models is being completely controlled by the model creators or else there would be at least one story by now of success that doesn't involve merely making the LLM products available to customers as a middle entity.

What's wrong with an agent being a glorified workflow? At Temporal (where I work), it seems plenty natural for agentic AI to just be AI worklows. Here's a video we put out this week demonstrating it: https://youtu.be/GEXllEH2XiQ (code at https://github.com/steveandroulakis/temporal-ai-agent).

Most “agents” are just starter prompts + a small set of tools that they can use to respond to things, like access to a database.

They’re workflow automation

Here’s a a talk from a month ago that covers a few use cases that are definitely beyond simple glorified workflow automation.

https://youtu.be/SpKtpW9TGF0?si=TRE6o7FfzCmhBuZq

The name for me is less important so much as can I have something that does my work for me. I've been starting to play with my own solutions between the 3 foundational modal companies. I've started to try to build my own stuff a bit, I think I need to learn more about apple scripting, also so far my experiments have required me to have multiple systems running to make it super easy for me.

You're all going to laugh at this stuff because it's so remedial and also clearly not agents but a couple things I've done... I won't say I really USE this stuff daily, I just play to see what I can do. I've figured out how to pass screenshots back and forth between modals (I have one computer take a screenshot every 30 minutes, and then send that screenshot to another machine, that machine is set up with a mouse hovering over the upload button on perplexity, it uploads the screenshot, and then perplexity does the work from the screenshot) An example of this that worked ok was I had chatgpt create all the themes for the social media schedule I needed to do this year, then I passed that screenshot to perplexity to do the searching on the web, and then I passed that to claud to write the tweet. This actually works ok-ish and I'm going to expand it a bit over the coming weeks I guess. Things like this are super helpful for weird hacks like that: https://github.com/BlueM/cliclick

Another thing I've found actually works pretty well is setting up two computers next to each other with ChatGPT voice mode, if you give them custom instructions to be sure to wait for the other one to be done talking, they don't interrupt each other and can get quite a bit of work done. Here is just a video of the mvp that I sent to a friend ages ago once I started playing with the idea: https://s.h4x.club/kpuzNkNL - I actually use this method of working quite often now, couple times a week at least, I find it's pretty helpful. If I knew how to put 4/5 modals together in one app and give them each custom instructions, I'd love to try building a team (if someone out there actually knows how to build this kinda stuff, I'm happy to help flesh out how the product would need to work, but I don't think it's super difficult to build at this point, I'm just not technical enough)

I'm not exactly clear what you're asking. Where do you draw the line between "workflow automation" and "doing work"? To me it just seems like a spectrum with rapidly moving goal posts.

A decade ago, enterprises had quite a lot of roles involving essentially moving data from one ERP screen to another. From what I'm seeing, these roles seem to be quickly disappearing, with a combination of proper API-based automation, GUI automation and most recently LLM "agents" in crucial steps.

And on a very different note, I as a developer could ask an AI tool such as Aider or Windsurf to perform a big refactoring or other code change, working autonomously across code changes and shell commands until it passes all tests - this is agentic behavior that I didn't have even a year ago.

I've created agents for the following:

- ICP / Sales Agent: I hired an offshore resource and built a GPT that they can send titles and other identifiers to, and it would say if it was in our ICP or not. I created it for a specific process that has outlined steps and FAQ from that person on things they have encountered, I plan on adding more questions and answers. This was super helpful on saving time on answering questions about titles / improving the results of their work.

- Domain Policy Scan (SPF, DKIM, DMARC): I scan domains and find SPF records and then use an Agent and a prompt to break out all the system tokens from the SPF to understand the systems companies are using. The prompt is a consent work in progress, but I have it done to be really consistent

Both have been really helpful to my overall workflow.

Just a buzzword for investors given we peaked with language models.

Chaining different prompts can be useful: calling that agents is purely marketing: these models are pretty dumb and don't have agency. I'd stay away from related frameworks

sales ops here. I was just tasked with figuring out how to use AI to use previous quotes to generate new quotes so sales people don't spend so much time creating quotes. Seems like the perfect thing for an agent. Anyone done this?

I have mentioned this on Twitter recently. My stream there is full of people talking about agents being the future, several posts on how to make them, but almost zero examples of any that they have built or used.

How can people tolerate the non-deterministic nature of AI agents in critical production workflows?

I don't still understand what really the hype is here, agent is just "A SMART FUNCTION CALLING ROUTER" at ground level, nothing more nothing less

Can be called as smart bot or bot 2.0 or something, but agent is way too much. Nothing really is agentic in agents

reminiscent of:

Ask HN: Are there any substantial examples of blockchain solving a real problem? (2020)

https://news.ycombinator.com/item?id=22914430

Cursor’s agent in the Composer workflow will check the linter as well as run tests in the “yolo” mode.

What makes it an agent is the feedback loop of making a change and then seeing the results and making further changes.

“Agents” are the new “middleware” / “workflow automation”.

What’s old is new again.

(Which is also why Salesforce is going big on agents. They acquired Mulesoft 8-years ago and agents are the next evolution of middleware)

"Workflow automation" is gonna be the goalpost to beat

Gemini with Deep Research has been really awesome to work with. It’s useful already now, but gets me very excited to see where agent work goes.

Unfortunately I'm one of those who haven't working stuff but hopefully will have one soon enough.

My thought process on agentic work is following- treating them for input-output operations to merge with deterministic processes.

To be more specific- from what I see in my non-tech industry, when you try to implement process management, people are quite good and terrible at implementing agreed processes at the same time. They are great at detecting deviation from process - when exception is needed and terrible to do same thing 1000th time in a row.

So on high level, I think agents should address automation, and detect when there is deviation from the process. In which case a human person should take over.

Tldr - I don't thing agentic workflows without human will be there any time soon. But we will have 2 human + agents replacing 10 human team

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