The moment I realized I'd already built one
A few months ago I was deep in the architecture of a voice-first AI technical interviewer — a platform that speaks to a candidate out loud, watches their keystrokes live over a WebSocket connection, nudges them with a hint when they're stuck, and at the end, outputs a structured hiring scorecard instead of a vague "went okay" note for a recruiter to guess at.
I wasn't building a chatbot. I was building something that observed, decided, and acted, on its own, inside a real workflow that used to require a human sitting in the room. Somewhere in the middle of wiring up the WebSocket editor deltas and the finite state machine that decided when to intervene, it hit me: this is an AI agent. I just hadn't been calling it that.
That realization is the reason I now spend most of my time building AI agents for businesses, not because I abandoned web development, but because I finally had a name for the kind of engineering I'd already drifted toward.
Where I actually started
For the last three-plus years I've been a full-stack and React Native engineer, not an "AI person." My days looked like:
- Shipping patient health flows at Tap Health — glucose tracking, meal logging, multi-step onboarding — in React, Next.js, and React Native, and chasing down the unglamorous bugs that actually break trust: timezone mismatches, state sync issues, navigation deadlocks.
- Building doctor portals, HR platforms, and admin dashboards from scratch at ZarvisGenix, wiring up role-based access control, real-time data sync, and secure APIs with Prisma and PostgreSQL.
- Sitting in rooms with investors and clinicians, translating what they actually needed into software that had to work correctly the first time, because in health tech, "close enough" isn't a shippable standard. None of that reads like an AI career on paper. But there's a thread running through all of it: I was always the person who got handed the workflow nobody had automated yet, and asked to make it disappear into software.
The pattern I couldn't unsee
At ZarvisGenix, I also built and integrated AI-powered speech-to-text pipelines, intelligent voice transcription that fed straight into automated workflows. It wasn't the headline feature of the project. It was infrastructure. But it was the first time I noticed something: the AI part of the system was never the hard part. The hard part was everything around it, the authentication, the rate limiting, the data schema, the error handling for when a transcription came back malformed, the part where the "smart" feature actually had to survive contact with real users and real edge cases.
That's a pattern I kept seeing repeat, project after project. Everyone wanted "AI" bolted onto their product. Almost nobody had thought through what happens when the AI is wrong, or slow, or needs to talk to four other systems to do its job. The gap wasn't a model problem. It was an engineering problem. And engineering problems are exactly what three years of production full-stack work had trained me to solve.
Why most "AI agents" on the market fall apart
Once I started paying attention, I noticed the same failure mode everywhere, on Fiverr, in client requests, in demos I got shown by other teams: someone wraps a system prompt around GPT or Claude, calls it an "agent," and ships it. It works beautifully in the demo. It falls over the moment a real user asks something slightly off-script, or the business needs it to actually do something instead of just reply, like check a calendar, update a CRM record, or hand off to a human at the right moment.
That's not an agent. That's a chatbot wearing an agent's name tag.
The AI Technical Interviewer project is the clearest proof I have that the difference is real. It didn't just generate text, it had to synthesize speech in under 300ms to feel human, track state deterministically so it never lost context mid-interview, evaluate code in a live sandbox, and produce a structured decision a hiring manager could actually act on. Every one of those requirements is a systems engineering problem wearing an AI costume. Get the engineering wrong and it doesn't matter how good the underlying model is.
Why I think this makes me a better fit than most agent builders
Here's the honest version, not the sales pitch version: a lot of people entered the "AI agent developer" space over the last year with no software engineering background at all. They learned a no-code automation tool, connected a couple of APIs, and started calling themselves AI agent specialists. Some of that work is genuinely fine for simple use cases. But it tends to break exactly where it matters most, at scale, under load, or when a client's business actually depends on the thing not failing silently at 2am.
I came at this from the opposite direction. I didn't start with prompts and work backward into engineering. I started as a production engineer, obsessive about frame budgets, latency, and deterministic state, and only later started pointing that discipline at AI systems. That means when I build an agent for a business, it's not a demo wearing a business's logo. It's:
- Connected to real tools, CRMs, calendars, databases, not just answering in a vacuum
- Built with proper error handling for the moment the model gets something wrong, because it will
- Documented and handed off cleanly, so a client isn't permanently dependent on me to keep it running
- Designed around the actual workflow a business has, not a generic template I resell to everyone ## What I actually build now
These days, most of my client work falls into one of these buckets:
- Lead qualification and booking agents that engage a lead the moment they message, ask the right questions, and book directly into a calendar
- Knowledge-base agents trained on a business's actual PDFs, docs, or website content, so answers are accurate instead of generic
- Multi-channel agents that work across a website, WhatsApp, and voice, from one underlying system
- Workflow automation using n8n to connect the agent into the tools a business already runs on None of this replaces the full-stack work. If anything, it's the same skill set pointed at a newer kind of problem. The React Native apps, the Next.js dashboards, the Prisma schemas, all of that is still the foundation. The agents just sit on top of it now.
If you're weighing the same move
If you're a web or full-stack developer looking at the AI agent space and wondering whether it's a real shift or just a rebrand, here's what I'd tell you: it's real, but only if you bring the engineering discipline with you. The market is already full of people who can wire up a chatbot in an afternoon. It is not full of people who can make that chatbot survive a real business's actual workflow. That gap is where the work is.
I build custom AI agents for businesses using GPT and Claude, you can see exactly what that looks like on my Fiverr gig. If you're trying to figure out whether your business actually needs one, or you're a fellow developer thinking about making the same jump, I'm happy to talk through it in the comments.
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