Ask HN: Are there any real examples of AI agents doing work
2025 is the year of agents. I’ve heard about SDR AI agents but not great things. Most “agents” sound like workflow automations that have been around forever. Anyone have an example of an “ai” agent which I understand to be intelligent that isn’t a glorified or rebranded workflow automation? Thx.
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'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.
Isn't that just simple glorified workflow automation? Shouldn't "agents" do and decide what to do themselves based on the holy prophecy of VC and AI Startups ?
Yes, for example from Anthropic's definition [1]:
> Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
> Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
[1] https://www.anthropic.com/research/building-effective-agents
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
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?
Sounds like a poorly thought out requirement. If you are tasked with speeding up the generation of quotes and find that AI can do the job well, that is perfectly reasonable. But if you are told what tool to use to make it happen, whomever tasked you with it doesn't understand that AI is a tool, not a goal. (I say that often enough, I may need to put it on t-shirts.)
In my pre-sales career, we just did copy and paste for spreadsheets and docs. Most quotes only require finding the nearest recent one and a replace-all for key bits of information followed by careful proof-reading.
Replace the word “agent” with algorithm and I agree. Why overcomplicate things?
Cause he can say he used AI and get a promotion and the company can put AI on the website and make stock price go up.
>> Anyone done this?
Yes, we have and more!
We sell maker and STEM education electronics, but the profit margins on products like Raspberry Pis, Micro:bits, and Arduinos are, well, pretty slim. This has pushed us to become extremely efficient; so much so that we ended up creating our own AI-agent-based ERP platform called Koi [1]
In essence, our work is built on the shoulders of giants like OpenAI’s Assistant API, Anthropic and Rails.
One of our standout demos is that certain objects (Orders, Quotes, Supplier Orders, Customers etc) in our database are assigned their own email addresses (using Rails' Action Mailbox[2]). Emails can be forwarded directly to these objects-whether it’s an order, a customer, or a supplier order.
From there, our agent, “Koi,” automatically extracts relevant information from emails and takes appropriate actions. For example, Koi can create a quote, attach a purchase order PDF to an order, or extract tracking information from supplier shipping confirmation emails to provide live tracking updates.
It also works the other way around; you can ask Koi to send a customer their tax invoice or inform them that a product they were interested in is out of stock, seamlessly handling typical customer service tasks.
Previously, we integrated speech-to-text functionality using the Whisper API, which made for an impressive demo.
Now, we’re taking it a step further by rebuilding our speech system to leverage OpenAI’s new WebRTC-based Real-time API. The key advantage here is that it comes with function calling support[3]. We already support a variety of automation features using barcodes[4], allowing users to scan a barcode and have Koi perform specific actions. This has proven to be an ideal area in the application to integrate tool use with the real-time API, creating even more powerful and efficient workflows.
Our ultimate goal is to integrate this system with Bishop, our product-picking robot[5].
[1] https://www.koi.app
[2] https://guides.rubyonrails.org/action_mailbox_basics.html
[3] https://platform.openai.com/docs/guides/realtime-model-capab...
[4] https://help.koi.app/article/54-barcode-driven-fulfillment
[5] https://piaustralia.com.au/pages/the-raspberry-pi-that-ships...
Your spiel here is much better than the website you've linked.
What you've linked sounds like you're selling a glorified shipping label printer.
I'm curious how this differs from standard TA/TMS systems that have been around for decades. I work in the space and there are plenty of TA/TMS systems that print shipping labels and fulfil orders, that update stock levels and send out tracking emails + SMS messages, integrate with carriers for shipment updates, that integrate with Shopify, eBay, Etsy, big commerce, etc.
They didn't need AI to do any of that. What's the advantage you're finding?
Here's an example that seems to operate in Australia:
https://www.shipstation.com/
Shipping is a fraction of what the system does. To completely automate shipping you need an understanding of inventory etc. To do automated customer service, you need knowledge of shipping, inventory etc.