I told myself at the start of this year that I was going to build something using AI. Not just use AI tools, but actually build something with it. I kept seeing people online make it sound simple enough that almost anyone could do it. So I figured, why not me?
What started as curiosity became a proper rabbit hole.
How It Started
It began with a YouTube video. I uploaded it as a reference point, a demonstration of what AI agents were capable of doing through Telegram. That video became the seed for everything that came after. I watched it, took mental notes, and thought to myself, "I can do this."
Well, I could! But not without a few surprises along the way.
Building Natalynn and Personal Assistant
What I did not expect was how quickly things could come together when you actually commit to it. In less than three hours, I had built working agents from scratch. Three of them, actually. Natalynn (client facing agent), Personal Assistant (response only to me - does my scheduling, search, read, draft emails and documents), and an Admin Agent (to handle more complex tasks). Not prototypes. Not demos. Actual, functional agents doing actual things.
That part genuinely surprised me. The tools available today make it possible to go from zero to something working in a sitting. I used Claude Code running through Windows PowerShell, set up an Ngrok server, and learned how to wire everything together working on .env files and API keys. None of that was in my vocabulary few weeks ago.
It was a real learn-by-doing experience. You figure out what each piece is for, why it matters, and how they connect, because nothing works until they all do.
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| Natalynn negotiating meeting times and booking it after checking with me |
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| Personal Assistant booking my calendar and giving dinner suggestions |
What Natalynn Can Do (for now)
Natalynn operates through Telegram as its primary interface. When an external party reaches out, the agent reads the intent of the message and routes it down one of three paths. For appointment requests, it checks my calendar for availability, proposes times directly to the external party, handles any back-and-forth negotiation autonomously, and only loops me in to approve or decline a finalized slot before confirming.
For service enquiries, it presents the full list of services with pricing, collects the person's contact details as a lead, and issues a quotation if they express interest in moving forward. Beyond business, if the person is in the mood for something lighter, the agent can throw in a joke. The whole point is that I only touch the workflow at the one moment that actually requires my decision, and everything else is handled automatically end to end.
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| Natalynn & Personal Agent workflow (Click to expand view) |
The Part Nobody Talks About Enough
Here is where I want to be straight with you, because I do not see this discussed honestly enough.
It is expensive.
Not in a casual, "oh you might spend a bit more than expected" kind of way. I mean genuinely expensive, in ways that sneak up on you.
Every time you make a change to a workflow, add new functionality, or fix a bug, you are using tokens. Every query your agent processes uses tokens. Every step in an automated workflow uses tokens. Even getting a simple reply back costs tokens as it hits the API. It all adds up, and it adds up fast.
The more you automate, the more steps a workflow takes, the more complex the task, the more it costs. Multiply the number of queries and tasks your agent handles per day across a full month, and you start doing some uncomfortable mental arithmetic.
Which led me to a question I did not expect to be asking myself after building all of this.
Is It Actually Worth It?
At a certain point, the cost of running agents and automation at scale starts to look a lot like the cost of hiring an actual person. And here is the difference: with a person, you can control the cost. A monthly salary is predictable. Token usage is not, especially when automation complexity grows over time.
What about you? Have you built an AI Agent or automate a workflow before? Would love to hear your experience.
