Building My First Creao AI Agent: Notes from a Curious Med Student

hassanismailtech — Thu, 05 Mar 2026 02:37:16 +0000

I was intrigued when someone in a WhatsApp group said, "We're going to build AI agents this weekend. "My honest first reaction was: cool, I have always been curious to build this, so why not.

And it turned out to be very much more than I thought. Here's what I learned and why I think the AI agent era is more important than most people realise.

First, a Quick History Lesson (Bear With Me)

We've had "AI" in our pockets for years. Siri. Alexa. Google Assistant. They were impressive the first time you asked one what the weather was or to set a reminder. Then you realised they couldn't do much beyond that without breaking down.

Then came workflow automation, tools like Zapier, Make, and n8n. The idea was beautiful: if X happens, automatically do Y. No code needed. This genuinely multiplied productivity for a lot of people. But it was brittle. Change one thing in the pipeline and the whole thing collapses. Automation is obedient, not intelligent.

Then Large Language Models arrived, and everything shifted.

Not because they're smarter than humans (that debate can wait). But because for the first time, the gap between "having an idea" and "having a working thing" became tiny. You don't need to know how to code an API call. You describe what you want, and the model figures out the execution.

That's the setup. Now here's where it got real for me.

The Agent I Built (And What It Actually Does)

During the community training session run by a trainer on Creao AI, the lesson was to build a Telegram bot that automatically finds news and sends it to a chat, no manual searching, no copying and pasting, no formatting.

I followed the steps:

Created a Telegram bot via BotFather, got my API token
Got my Chat ID from a user ID bot
Pasted a structured prompt into Creao.AI with my token, chat ID, and topic filled in
Ran it

It worked. The agent searched Google News, pulled two fresh stories, summarised them in plain English, formatted everything neatly, and posted directly to my Telegram on its own.

But here's what I actually did with that knowledge:

I adapted it into the Webibyte ByteTips Bot.

Instead of generic news, my bot finds fresh bite-sized tech tips relevant to my startup's audience, developers, digital marketers, and small business owners in Nigeria and delivers them to a Telegram channel automatically. It's branded. It's useful. It runs while I'm doing personal studies.

That's the part that still gets me. A workflow that would have taken me 2–3 hours of manual research, writing, and posting runs itself.

I Also Automated Gmail to WhatsApp

The trainer also walked us through connecting Gmail responses to WhatsApp. I ran that one as-is, without modification, and it worked exactly as described.

Two agents. Two evenings. No code written.

I want to be careful not to oversell this. I didn't build something from scratch. I followed a structured tutorial, filled in my credentials, and understood what each part was doing. But that's also kind of the point, the barrier is now low enough that understanding the concept IS enough to build something real.

So What Is an AI Agent, Actually?

Here's the simplest, honest answer I can give:

A standard AI interaction is a transaction. You ask → it answers. Done.

An AI agent is a loop. You give it a goal → it plans → it uses tools → it acts → it evaluates → it adjusts. Over and over, without you approving each step.

The components:

A reasoning core (the LLM doing the thinking)
Tools (APIs, browsers, databases, etc., whatever the agent can call)
Memory (short-term for the current task, long-term for persistent context)
Action execution (actually doing the thing in the world)
Feedback (evaluating what happened and adapting)

What makes this different from automation is the reasoning layer. An automation fails when it hits something unexpected. An agent tries to work around it.

Why This Matters More

In Nigeria, for instance, the physician-to-patient ratio is critical. Administrative burden on medical staff is enormous. A lot of knowledge work that could be automated is still done manually, not because people don't know better, but because the tools either didn't exist or required expertise that most people don't have.

AI agents can change that math.

As a medical student and web developer. I don't have the hours to manually curate and post content, monitor industry news, AND still do my personal study. Agents give me leverage I couldn't otherwise afford.

And crucially, I believe the most powerful agents will not be built by the best engineers. They'll be built by people who most deeply understand the problem they're solving. A cardiologist building a clinical triage agent will out-build a generalist developer every time. My understanding of health communication in Nigeria is an asset in this space, not an afterthought.

That's democratising in a way that feels real, not theoretical.

What I'm Watching Next

A few developments I'm genuinely tracking:

Multi-agent systems: where specialised agents hand off tasks to each other. One agent researches, another writes, and another reviews. This is where things get interesting for complex knowledge work.

Persistent memory: agents that remember context across sessions, accumulating knowledge about you, your preferences, and your ongoing projects. Right now, most agents are stateless. That's changing fast.

Agents in African healthcare: structuring patient data, flagging anomalies, generating referrals, monitoring disease signals. The clinical and public health applications here are enormous and largely unbuilt.

One Honest Caveat

Security matters more than people admit in beginner agent tutorials.

Your bot token is a credential. Your Chat ID is sensitive. Any agent that authenticates with a third-party service needs those credentials to be kept private.

This isn't advanced knowledge. It's foundational. Treat it that way from day one.

TL;DR

AI agents aren't just smarter chatbots, they plan, act, and iterate toward a goal
I built two as a learner: a custom Webibyte ByteTips Bot for Telegram and an automated Gmail-to-WhatsApp pipeline
The real barrier now is understanding the problem you want to solve, not understanding the code
Domain expertise, clinical, cultural, and contextual is the actual competitive edge in the agentic era
For builders in Africa, this isn't just exciting. It's strategic.

I'm Hassan Ismail, a medical student, web developer, WEBIBYTE founder, and someone working at the intersection of health and technology in Africa. Find me at @hassanismailtech or drop a comment below.

BioSecure CLI: Building a Medical Command Center in 32 Minutes with GitHub Copilot Agents

hassanismailtech — Mon, 16 Feb 2026 07:16:36 +0000

This is a submission for the GitHub Copilot CLI Challenge

What I Built

I built BioSecure CLI, a terminal-based "Medical Command Center" designed to solve three critical failures in the Nigerian primary healthcare supply chain:

Messy Patient Data: A validator that automatically cleans and formats Nigerian phone numbers (e.g., converting 080... to +234...) using Regex.
Slow Triage: A clinical decision support tool that calculates BMI and flags Hypertensive Crises based on vitals.
Drug Stockouts: A logistics engine that tracks vaccine inventory and triggers "Critical Low" alerts to prevent expiry and shortages.

This isn't just a script; it's a prototype for a Closed-Loop Health Ecosystem intended to reduce drug "leakage" and improve patient outcomes in rural Nigeria.

Demo

Here is the entire development lifecycle, from empty folder to production-ready MVP, captured in real-time.

1. The Planning Phase (The "Architect")

I didn't start by writing code. I started by telling the Agent my vision. I asked for a 3-module system for Triage, Validation, and Logistics.

2. The Execution (The "Builder")

The CLI Agent autonomously created the project structure, set up the Python virtual environment, and installed dependencies (rich, pytest). I didn't type a single mkdir command.
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3. The Logic Generation

I tasked the agent with complex logic, such as a "Phone Number Validator" for Nigerian formats. It wrote the code and the tests simultaneously.

4. The Result (32 Minutes Later)

In exactly 32 minutes and 47 seconds, the Agent delivered a fully tested, documented, and operational CLI tool.

Link to Source Code on GitHub

My Experience with GitHub Copilot CLI

As a medical student, I usually find terminal environments intimidating. My experience with the new Agentic Copilot CLI was transformative because it shifted my role from "Coder" to "Director."

Autonomy: I didn't have to look up syntax for the rich library or pytest. The agent knew what to install and how to use it.
Test-Driven Development (TDD): The agent automatically wrote tests for every module (Triage, Logistics, Validator) before I even asked. This ensured my medical logic was safe for patient use.
Documentation: It auto-generated a README.md and QUICKSTART.md at the end, saving me hours of writing.

This tool didn't just help me write code; it helped me build a product before my morning lectures even started.

Next Steps

This MVP is the foundation for a larger "BioSecure" ecosystem, including:

Phase 6: Database persistence (SQLite), which the Agent already planned for me.
Hardware Integration: Connecting the Logistics module to IoT Smart Shelves for real-time vaccine tracking.

Forem: hassanismailtech

Building My First Creao AI Agent: Notes from a Curious Med Student

First, a Quick History Lesson (Bear With Me)

The Agent I Built (And What It Actually Does)

I Also Automated Gmail to WhatsApp

So What Is an AI Agent, Actually?

Why This Matters More

What I'm Watching Next

One Honest Caveat

TL;DR

[Boost]

BioSecure CLI: Building a Medical Command Center in 32 Minutes with GitHub Copilot Agents

hassanismailtech ・ Feb 16

BioSecure CLI: Building a Medical Command Center in 32 Minutes with GitHub Copilot Agents

What I Built

Demo

1. The Planning Phase (The "Architect")

2. The Execution (The "Builder")

3. The Logic Generation

4. The Result (32 Minutes Later)

My Experience with GitHub Copilot CLI

Next Steps