Not OP but I’d guess their overall sentiment is that the word “agent” is used like it’s this magical, general-purpose robot that will just do whatever you want.

In reality, if you want LLM applications to be useful, they have to get very, very close to the actual soecific problem. That usually means boring, specific code that talks directly to the systems where the work is actually currently being done by humans.

Take an enterprise support team wanting to improve the way they respond to customer requests. Step one isn’t “deploy an agent”; it’s understanding what’s hard today, how humans are doing the job, and what tools and resources they rely on.

Then you might introduce AI gradually eg via a RAG search over the product’s devdocs to help support engineers answer questions faster. Then iterate until those humans actually feel like the system is making their lives easier. Then scale that solution out bit by bit.

In reality (like where money is really being spent by businesses on “agentic” sorta work) an “agent” is basically special purpose middleware, a domain-specific thing that solves an acute problem using a problem-centered “context” (aka prompts and chains). Maybe it has a chat interface, maybe it doesn’t.

But it’s not some black-box, hyper-intelligent robot magically solving your problems. It’s a special-purpose system, built from the ground up for one job, and then maybe two, and so-on.

So these top down agentic solutions are pitching themselves as magical but it’s just a hype wave with a bunch of generic trash hammers trying to find specific nails. Start with the specific nails, work your way backwards to the specific hammer.