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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsI didn’t leave full-stack development behind. I began applying it to AI agents: software that uses context, maintains state, connects to business tools, and can take actions rather than only return a reply. That shift became clear to me while building a voice-first technical interviewer.
By Valipireddy Kowshik, based on his first-person account published on DEV Community on September 16, 2026.
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What made the shift feel real
After more than three years working in full-stack development and React Native, I was spending more of my time on business AI agents. My earlier work, as I describe it, included patient health flows at Tap Health, doctor portals and HR and admin platforms at ZarvisGenix, and speech-to-text pipelines that fed automated workflows.
The turning point was a voice-first technical interviewer. It speaks with a candidate, observes live keystrokes through a WebSocket connection, offers hints when the candidate is stuck, and produces a structured hiring scorecard. While I was working on editor updates over WebSockets and a finite-state machine to decide when the system should intervene, I recognized the work as building an AI agent. As I put it, “I wasn’t building a chatbot.”
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Why full-stack skills still matter
Integrating a model is only one part of making an agent useful in a real business workflow. The engineering work also includes authentication, rate limiting, data schemas, malformed outputs, error handling, deterministic state, and connections to tools such as calendars, CRMs, and databases. These are not separate from the AI feature: they determine whether it can function reliably within the systems a business already uses.
That is why I see my move as an extension of full-stack engineering, not a departure from it. The same concerns—how data moves, how services fail, and how a user completes a task—still apply. The new challenge is coordinating those pieces with a model that can interpret a request and decide what to do next.
A practical distinction between a chatbot and an agent
I use a practical heuristic, not a formal definition: an assistant that only replies is different from a system that observes context, makes decisions, takes actions, and hands off when appropriate. A prompt wrapped around a model may produce conversational answers, but that alone does not make it capable of reliably handling off-script requests or acting on connected systems.
For a business workflow, the more useful questions are whether the system can:
- Use relevant context and maintain state across the interaction.
- Connect to the tools or data needed to complete the task.
- Handle errors and unexpected inputs without losing control of the workflow.
- Take an appropriate action or escalate to a person when needed.
This distinction reflects my experience and how I evaluate the work; it is not an independently tested comparison of agent-building products.
What the interviewer required
The interviewer brought together several parts of software engineering: live communication, state management, code evaluation, and a structured outcome. I reported that it needed to synthesize speech “in under 300ms,” track state deterministically, evaluate code in a live sandbox, and produce a hiring decision. That timing is my project claim, not a published benchmark: I did not provide a measurement method, test conditions, or independent validation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.The kinds of agents I build now
My current work, as I describe it, spans several business use cases:
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- Lead qualification and booking: agents ask questions, qualify a lead, and schedule an appointment.
- Knowledge-base answers: agents use a business’s PDFs, documents, or website as a source of information.
- Multichannel support: agents work across channels such as a website, WhatsApp, and voice.
- Workflow automation: I use n8n to connect agents with tools a business already has.
I say I use GPT and Claude for custom agents. The point is not that a particular model removes the engineering work; the work still includes integrating it into a workflow and accounting for state, tools, and failure cases.
Is this a real shift or a rebrand?
For a web or full-stack developer considering agent work, the label matters less than the system being built. If the application only answers questions, it may be a chatbot or assistant. If it also tracks the task, uses connected tools, takes bounded actions, manages errors, and knows when to hand off, it has a broader workflow role. In my case, building the interviewer made that role visible to me—and my existing engineering skills gave me a foundation for it.
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