Who’s Actually Building AI Agents?

Alright, here’s the thing.

People keep saying they’re building AI agents in a weekend. You see it all over LinkedIn and Twitter. “My business runs itself now. Just me and a few agents.” That kind of stuff.

Is it possible? Maybe in some cases, sure. But if you’ve ever actually built something for production, especially inside a company that has real customers, real systems, real stakes… you know it’s never that simple.

It’s Not Just About Creating Prompts

People think building an agent is just writing some good prompts but it’s not even close.

A prompt is basically a suggestion that tells the model how to respond, what kind of tone to use, maybe what format you want. It doesn’t give the model tools. It doesn’t give it memory. It doesn’t teach it how your systems work.

If you just want to summarize a doc or answer a quick question, yeah, a prompt is enough. But the moment you want the thing to do something, like actually take action, make decisions, use your tools, prompts alone fall apart.

An agent needs structure and needs to know what tools it can use, when to use them, how to pass data around. It has to handle bad inputs and has to know when it’s stuck or confused. And it can’t just forget everything five seconds later so there has to be memory and storage, some state, something holding the thread together.

You can’t get that from a prompt. You need to define it, build it, wire stuff up, and expect to fix it when it breaks, because it will.

Honestly, It Feels a Lot Like Regular Software

You still have to figure out what the thing is even allowed to do. Write some handlers, hook it into your systems, deal with auth (which is always a mess), set up logging so you’re not sitting there wondering what went wrong when it breaks. And yeah, it will break.

It’s not some AI-powered shortcut to avoid all the usual work. It’s software and you still need to plan it out. You still need people who know what they’re doing. It’s the same old mess, just with more unpredictability.

Context, Memory, Feedback. All That Boring Stuff.

You realize real fast that the agent needs memory. Not just “remember the last message” memory, but actual state. Like, what it did five minutes ago, or what it learned last week.

And feedback. Because it’s going to get stuff wrong. Sometimes embarrassingly wrong. And you’ll need a way to catch that before it causes damage. Logging helps. Monitoring helps. Honestly, a human watching it for a while helps too.

But none of this gets shown in the hype posts. Because nobody wants to talk about the hours spent debugging weird edge cases or watching your agent make the same mistake for the tenth time.

You’re Probably Not Doing It Alone

This part always gets glossed over. But if you’re doing anything even slightly serious with agents, it’s not just you.

You need someone who understands your systems. Someone who’s watching the performance. Someone who can figure out what went wrong when the agent starts behaving strangely.

And yeah, maybe you can wire up a basic version by yourself. But the second you want it to be reliable? Or secure? Or scalable? You need a team. Doesn’t have to be huge. But solo agent startups… let’s just say they leave a lot unsaid.

Some of It Feels More Like Theater

I’ll just say it. A lot of what you’re seeing out there is theater. It’s a wrapper around ChatGPT with a few buttons. Or it’s a scripted demo that falls apart if you go off the rails.

There’s nothing wrong with building prototypes. I love tinkering. But let’s not confuse prototypes with products. If your agent can’t handle errors, or log its own actions, or escalate to a human when it’s unsure… it’s not running your business. It’s pretending.

You Still Only Get Two

The old rule still applies. Fast, good, cheap. Pick two.

You want something good and fast? It won’t be cheap. You want cheap and fast? It won’t be good.

And AI agents? Yeah, they might move you a little faster. But they don’t break the rule. They just shift where the work shows up.