By now, you know the difference between an AI agent and something that’s “agentic”.
Now the obvious question is:
How do you actually recognise something that’s truly agentic?
Because right now, everything is being called “agentic” — even when it’s not.
Here’s a clue:
There are four things that make an AI system feel agentic.
If these are missing, you’re just looking at a smarter chatbot.
Let’s clear this up properly.
Because most people mix these two up.
You’ll hear terms like:
And it all starts sounding like the same thing.
It’s not.
Let’s keep this very simple.
An AI agent is the thing you build.
It’s a tool or system that does a specific job.
Now that the introduction to this series is over….
Most of us use artificial intelligence (AI) like this:
We ask a question → it gives an answer.
“Write this email”
“Summarise this article”
“Give me ideas”
And honestly, it’s useful. Tools like ChatGPT have already made work faster and easier.
But here’s the catch.
You, the human, are still doing most of the work.
You decide what to ask.
You decide what to do next.
You connect the dots.
AI is helping… but you’re still driving.
Let’s be honest.
Most people right now are either confused about AI… or overwhelmed by it.
You’ve probably tried tools like ChatGPT, maybe generated some content, maybe asked it a few questions. It feels impressive. But also a bit… limited.
You still have to do most of the thinking.
Most of the connecting.
Most of the actual work.
Now suddenly, there’s a new term floating around — “agentic AI”.
Sounds fancy. Slightly intimidating. And honestly, a bit overhyped.
But here’s the thing. Behind all that jargon is a simple shift that actually matters.
AI is slowly moving from something that just responds…
to something that can take initiative, think in steps, and get things done.
And if you understand this early, you’re not just “using AI” anymore. You’re building systems that can actually work for you.
That’s what this series, ” How To Build Your First AI Agent” is about.
No heavy tech talk.
No complicated theory.
No assuming you’re an engineer. Or coder.
Just a clear, step-by-step way to understand:
Whether you’re a creator, a professional, or just someone curious about where things are heading — this is designed to make you feel like you’re figuring it out, not falling behind.
Think of this as a guided journey.
By the end of it, you shouldn’t just “get it”.
You should be able to build something small, useful, and maybe even impressive.
Let’s start simple.
Ready for Part 1?
Disclaimer
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1. Generative AI
Generative AI refers to systems that can create new content—text, images, videos, music, and even code—based on patterns learned from existing data. Tools like chatbots, image generators, and video creators fall into this category. In 2026, gen-AI is widely used in marketing, education, entertainment, and product design, helping people move from idea to output in seconds rather than hours.
2. Large Language Models (LLMs)
LLMs are the brains behind modern AI chat systems. They are trained on massive amounts of text data to understand and generate human-like language. What makes them powerful today is their ability to reason, summarize, translate, and even simulate expertise across domains. In 2026, LLMs are embedded in everything—from workplace tools to personal assistants—making communication with machines feel natural.
3. Multimodal AI
Multimodal AI can process and combine different types of data—like text, images, audio, and video—at the same time. For example, you can show an AI a picture, ask questions about it, and get spoken answers. This makes AI more human-like in how it understands the world. In 2026, multimodal systems are key to applications like smart assistants, healthcare diagnostics, and content creation.
4. AI Agents
AI agents are autonomous systems that can perform tasks on your behalf. Instead of just answering questions, they can plan, take actions, use tools, and complete multi-step goals—like booking travel, managing workflows, or running business operations. In 2026, AI agents are becoming digital co-workers, capable of handling repetitive and even moderately complex tasks with minimal supervision.
5. Retrieval-Augmented Generation (RAG)
RAG is a technique that improves AI accuracy by combining generation with real-time information retrieval. Instead of relying only on pre-trained knowledge, the AI pulls relevant data from databases or the internet before answering. This reduces hallucinations and makes responses more reliable. In 2026, RAG is widely used in enterprise AI systems, customer support bots, and research tools where accuracy is critical.
It was only a matter of time. Wikipedia, the Internet’s most trusted crowdsourced encyclopedia, has finally drawn a firm line in the digital sand—and this time, it’s aimed squarely at artificial intelligence.
Frustrated by made-up facts and sketchy citations, Wikipedia has put its foot down: no more AI-written articles. Reports say the platform has barred its global community of volunteer editors from using AI tools to generate or rewrite entries. AI can still lend a hand with translations or light grammar tweaks — but when it comes to actual content, humans are very much back in charge.
Click here to read the rest of the story.
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-Breaking Down AI For Everyone-
This could bring a bit of a cheer to our members.
Recent surveys conducted by the U.S. Federal Reserve and the St. Louis Fed show “no clear evidence” that artificial intelligence (AI)adoption has led to widespread job losses. In fact, industries with higher AI uptake are reporting faster productivity growth on both sides of the Atlantic. Job postings data also indicate that firms embracing AI are not reducing hiring compared to others, suggesting that automation is not yet driving the slowdown in recruitment.
Philippines and India
According to a recent blog post by James Pethokoukis, senior Fellow DeWitt Wallace Chair Editor, AEIdeas Blog, Apollo Global Management had tracked unemployment trends in two economies heavily exposed to outsourced service work — call centers and back-office operations. Despite predictions that generative AI would devastate these sectors, neither Manila nor India had shown signs of labor-market deterioration. Analysts noted that if automation were truly eliminating jobs at scale, these markets would be the first to feel the shock.
Programmers Worldwide
There was one notable exception in programming jobs. Federal Reserve research showed employment growth among coders had slowed significantly since the launch of ChatGPT in 2022. While programmer employment continued to rise, the pace had decelerated, reflecting occupation-specific pressures rather than broader industry weakness.
Across the U.S., Europe, India, and the Philippines, AI’s labor market impact remains muted. While certain occupations —particularly programming — are experiencing slower growth, broader fears of mass displacement are not yet supported by data. Policymakers face the challenge of balancing vigilance with evidence-based action as AI adoption accelerates.
Here’s a clear breakdown of the impact of AI on jobs in each country or region mentioned in the article you’re viewing:
Source: https://www.aei.org/economics/ai-job-panic-still-outruns-the-evidence/
AI coding assistants have quickly become a part of everyday life for developers. What started as simple autocomplete tools has evolved into something much more powerful; tools that feel like intelligent agents, almost like having your own “Agent Smith” sitting beside you, helping you write, debug, and understand code.
One of the most widely used tools today is “GitHub Copilot”. It acts like a reliable pair programmer who is always available. As you write code, it suggests entire lines or even full functions based on your comments. For many developers, this means spending less time on boilerplate code and more time focusing on logic and problem-solving. You can simply write a comment describing what you want, and Copilot often fills in the rest in seconds.
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An AI co-scientist is not just a tool that crunches data. It’s a system that actively participates in the scientific process. Instead of only analyzing results, it can:
Think of it less like a calculator and more like a junior (and increasingly senior) research partner that never sleeps and can read millions of papers instantly.
Several trends have converged to make AI co-scientists possible:
a. Explosion of scientific data
Modern science generates far more data than humans can process alone (genomics, climate models, particle physics, etc.).
b. Advances in AI models
Large-scale AI systems can now:
c. Integration with tools
AI is no longer isolated. It can: