I keep seeing tons of content about AI agent development everywhere. People post tutorials about frameworks, system design, and prompting techniques all the time. But here’s what bugs me - where are the actual working agents that regular people can use? I see lots of theory but very few real examples. What’s going on here? Maybe the agents are too specific for personal projects? Could there be legal issues or security worries? Are creators just making content for clicks instead of real solutions? Or maybe they don’t want to give away their competitive advantage? Anyone here actually making these agents? What keeps you from sharing your work publicly? Am I looking in the wrong places for actual working examples?
The gap between AI agent tutorials and real deployments? It’s all about infrastructure costs and reliability. I’ve built several agents that work great in controlled setups, but scaling them for public use needs serious server resources and 24/7 monitoring. Most devs just create proof-of-concepts that show what’s possible - they’re not built for production. There’s also liability. Once you deploy an agent publicly, you’re on the hook for whatever it does or breaks. The jump from working prototype to reliable service is way bigger than tutorials let on. Plus, lots of functional agents are being built inside companies for internal use, not public release. That’s why you see more educational content than actual products.
Most working AI agents aren’t the flashy general assistants everyone talks about - they’re boring, specialized tools doing one thing well. I’ve spent the past year building automation agents and learned that the successful ones have really narrow focus: customer service bots, data processing, content moderation. Problem is, these don’t make cool demos or go viral. I released a document processing agent publicly once. Big mistake. Within weeks, users found edge cases I’d never thought of, API costs went through the roof, and I spent more time debugging their specific problems than actually building anything new. Turns out maintaining a public AI agent is basically running a SaaS business with all the same headaches. That’s why you see way more tutorials than actual products - teaching people about agents scales way better than actually running them.
frankly, that’s so true! i tried to code one but encountered so many issues when it came to real world apps. they look amazing in demos, but real life usage is a total diffrent story. it’s like, the tech is still catching up with the hype.
Honestly? Most devs just build resume projects, not real products. GitHub’s full of “AI agent” repos that are basically chatbots with fancy names. Everyone wants to build the next Jarvis instead of solving boring problems that actually matter.
You’re hitting on something I’ve been dealing with for years. Working agents exist - they’re just locked away in enterprise environments where you’ll never see them.
I’ve shipped probably a dozen AI agents in my career, but they all solve internal problems. We have agents that route support tickets, manage deployments, and handle code reviews. They work great because we control every piece of the pipeline.
Public ones fail because of token costs alone. I built an agent that helped with code refactoring and put it on GitHub. Within a month, my OpenAI bill hit $800 because people were feeding it entire codebases. Had to shut it down.
There’s also the integration nightmare. A truly useful agent needs to connect to your email, calendar, databases, APIs. That means OAuth flows, permission management, and security audits. Most developers don’t want to deal with that complexity for a side project.
The successful public agents I’ve seen are either heavily rate limited or charge money upfront. Zapier’s AI stuff works because they already have the infrastructure. Individual developers just can’t compete with that level of operational maturity.
Check out some of the workflow automation platforms if you want to see agents that actually work. They’re just not branded as AI agents.