I do contracting work, we're building a text-to-sql automated business analyst. It's quite well-rounded: it tries to recover from errors, allows automatic creation of appropriate visualisations, has a generic "faq" component to help the user understand how to use the tool. The tool is available to some 10.000 b2b users.

It's just a bunch of prompts conditionally slapped together in a call graph.

The client needed AGENTIC AI, without specifying exactly what this meant. I spent two weeks pushing back on it, stating that if you replace the hardcoded call graph with something that has """free will""", accuracy and interpretability goes down whilst runtimes go up... but no, we must have agents.

So I did nothing, and called the current setup "constrained agentic ai". The result: High fives all around, everyone is happy

Make of that what you will... ai agents are at least 90% hype.

The right approach to build toward agents is to start with something that gives pretty good responses to prompts and build up an agentic mode to let it do more and more in response to each prompt. It should be thought of as extending how much you get per prompt, and doing so by chaining together components you've already worked at making to good at.

Cursor (the LLM powered VS Code fork) has an agentic mode and they are doing this the right way. The normal chat window is good at producing changes to your code, and at applying them, at looking at lints, at suggesting terminal commands, at doing directory listings or RAG on your codebase. Agentic mode is tying those together to do more of the work you want with fewer prompts from you.

1. Its still perceived as an issue of competitive advantage

2. There is a serious concern about backlash. The public's response to finding out that companies have used AI has often not been good (or even reasonable) -- particularly if there was worker replacement related to it.

It's a bit more complicated with "agents" as there are 4 or 5 competing definitions for what that actually means. No one is really sure what an 'agentic' system is right now.

I look at my workplace and I see places where they might fit in but if the reliability isn’t 99.5% they won’t be trusted and I think that’s a problem.

I made a toy in n8n that collects transactions in YNAB via API and matches them to Amazon orders in GMail. It then uses GPT-4o with vision to categorize the product pictures according to my budget’s categories but I have to add the order link to the transaction memo and add a flag for human review because it’s only 80% or so. It has sped up the workflow for sure but nowhere near good enough to set it and forget it.

If you ask two different people in the AI space to define "agent" you almost always get two slightly (or significantly) different definitions!

Here are just some of the definitions I've seen over time: https://news.ycombinator.com/item?id=42216217#42228364

For the purpose of this thread the most cynical definition, "LLMs that do something useful", might actually be the best fit!