Agentic AI — Yet Another Buzzword?
Data Engineering and Beyond
Hello everyone, welcome back!
Let me start with a little story from myPizza bakery.
Imagine this: You’re running the bakery, and during a busy weekend, you need to decide how much dough to prepare, which toppings to stock up on, and how many delivery staff to call in.
Usually, you’d rely on sales data, some guesswork, and a bit of luck to get it right.
Now imagine if you had an AI assistant that didn’t just follow your instructions but actually made those decisions for you.
Not only that, it monitors social media trends, predicts which toppings will be in demand, dynamically orders supplies, and adjusts staffing schedules — all without you lifting a finger.
Sounds like a dream, right? Or maybe a little scary?
This is what people mean when they talk about agentic AI.
Unlike traditional AI systems that are designed to follow specific instructions, agentic AI is envisioned as something that can make its own decisions, set its own goals, and act independently based on the environment it operates in.
At its core, agentic AI aims to mimic human agency — the ability to think ahead, adapt, and take action in complex scenarios.
It’s like giving your AI assistant at myPizza bakery a mind of its own, allowing it to solve problems you haven’t even noticed yet.
But is this vision truly within our reach? Or is it just another buzzword designed to grab our attention and sell the next wave of technology?
In today’s post, we’re going to critically analyze the concept of agentic AI.
We’ll explore whether it’s a genuine breakthrough, a hyped-up fad, or somewhere in between.
By the end, you’ll have a clearer picture of what agentic AI means, what it promises, and whether it’s something to embrace — or approach with caution.
Let’s dive in!
1. Understanding the Core Idea
So let’s take a closer look at what agentic AI really means and how it compares to the AI we use today.
What is Agentic AI?
Most of us are familiar with narrow AI, the kind of AI that powers tools like your GPS app, Netflix recommendations, or even the automated systems at myPizza bakery that optimize oven temperatures or predict daily demand.
Narrow AI is great at performing specific tasks, but it can’t think beyond its programming.
Agentic AI, on the other hand, is envisioned as something entirely different.
It’s designed to mimic human agency, meaning it can independently analyze situations, set goals, adapt to new circumstances, and even improve itself over time.
Think of it as an AI manager at myPizza bakery — not just taking orders but making decisions, finding innovative ways to increase efficiency, and even creating new business opportunities.
Examples and Current Applications
We’re starting to see glimpses of agentic AI in tools like AutoGPT and ChatGPT with plugins.
For instance, AutoGPT can autonomously break down a high-level goal — say, improving customer experience at the bakery — into smaller tasks, research solutions, and execute them without constant human input.
However, these systems are still far from perfect.
They often lack true autonomy and depend on the quality of the goals and data provided by humans.
For example, AutoGPT might suggest optimizing toppings by switching suppliers but overlook logistical challenges or brand quality standards.
Similarly, ChatGPT with plugins can assist with dynamic pricing or inventory updates, but it can’t yet account for complex, real-world uncertainties.
Promises Made by Agentic AI
Despite its limitations, the potential of agentic AI is exciting. It promises to:
1. Automate Complex Decision-Making:
Imagine a system that handles staffing, supply chains, and marketing strategies at the bakery without needing constant oversight.
2. Scale Human-Like Thinking Across Industries:
In healthcare, agentic AI could prioritize emergency cases in real-time. In finance, it might dynamically rebalance portfolios based on market trends.
At myPizza bakery, this could mean smarter scheduling that adapts to weather conditions, festive seasons, or even sudden social media trends — decisions that are currently hard to automate effectively.
Agentic AI could transform how we approach challenges, but it’s crucial to understand where it stands today and where it might fall short.
With that foundation, let’s move on to critically evaluating whether agentic AI is truly a game-changer or just another buzzword.
2. The Case for Agentic AI: Breakthrough or Buzzword?
Now that we’ve got a basic understanding of what agentic AI is, let’s explore whether it’s truly a breakthrough or just another buzzword riding the AI hype train.
Hype Indicators
First, let’s talk about the hype.
Agentic AI has become a hot topic in marketing and media.
You’ve probably seen headlines like “The Future is Autonomous AI” or “Agentic AI Will Replace Your Job!” Startups are jumping on the bandwagon, making grand promises to attract funding.
For example, imagine a startup pitching to myPizza bakery:
“Our agentic AI will increase profits by 300%, streamline every operation, and predict customer preferences with 100% accuracy!”
Sounds amazing, right? But here’s the problem: most of these claims lack substance.
Many of these systems are repackaged versions of existing AI, with a fancy new label slapped on.
They might automate repetitive tasks, but they don’t possess the true independence or adaptability that defines agentic AI.
We’ve seen this pattern before — blockchain, metaverse, you name it.
The buzz often outpaces the technology, and companies rush to ride the wave without delivering real innovation.
Real Potential
That said, it’s important to acknowledge the genuine promise of agentic AI. While much of the hype is exaggerated, there are areas where this technology could be transformative.
1. Self-Driving Cars: Imagine an AI that doesn’t just drive but also decides the best routes based on live traffic, fuel efficiency, and passenger preferences — all without human intervention.
This level of adaptability is what agentic AI aspires to achieve.
2. Personalized Medicine: Agentic AI could revolutionize healthcare by dynamically adjusting treatment plans for patients based on real-time data like their genetic profile, lifestyle, and medical history.
3. Supply Chain Automation: At myPizza bakery, agentic AI could take supply chain management to the next level.
Instead of merely forecasting demand, it could independently analyze supplier reliability, transportation costs, and even global events like weather disruptions.
Imagine an AI that decides to source extra cheese from a different vendor because it predicts a weekend surge in orders due to a local festival.
Early Successes and Research
We’re already seeing early successes in some of these areas.
At myPizza bakery, for instance, existing AI tools can help optimize inventory or schedule staff.
Agentic AI could take this further, not just reacting to data but proactively finding opportunities.
Ongoing research is also exploring how these systems can improve, such as by integrating better decision-making models or refining their ability to learn from real-world feedback.
So, while agentic AI isn’t fully realized yet, its potential is real.
The challenge is separating genuine progress from overblown promises and focusing on where it can truly add value.
With that in mind, let’s turn to the risks and limitations of this technology.
3. The Case Against Agentic AI: Risks and Limitations
While agentic AI has exciting potential, it’s far from perfect. Let’s take a closer look at its limitations, risks, and why it might not live up to the hype — at least for now.
Technological Limitations
The first major challenge is that today’s AI lacks true understanding.
Agentic AI might seem independent, but it’s still limited by human-defined objectives.
For instance, at myPizza bakery, imagine we program an AI to optimize profits.
It might decide to cut costs by reducing cheese quality or overloading staff with work — because it doesn’t truly understand the broader goal of keeping customers happy and employees motivated.
And then there’s the issue of biases in the data.
If the data we feed into the system reflects past mistakes or skewed priorities, the AI will only repeat and amplify those errors.
For example, if historical sales data shows that only pepperoni pizzas sell well on weekends, the AI might stop promoting other options, limiting customer choice.
Ethical and Societal Risks
Next, let’s talk about ethics and accountability.
Over-reliance on AI for critical decisions can be dangerous, especially in situations with moral or ethical dilemmas.
Imagine if myPizza bakery’s AI has to decide between fulfilling a large corporate order and a smaller community event during a supply shortage.
How does it prioritize? What if its decision harms our reputation or alienates loyal customers?
Then there’s the risk of unintended consequences.
If the AI pursues a flawed goal autonomously, things can quickly go wrong.
For instance, the AI might decide that maximizing delivery speed is the top priority and overworks delivery drivers, causing burnout and high turnover.
While the AI achieves its immediate goal, it creates long-term problems for the business.
The Fad Effect
Finally, let’s address the possibility that agentic AI might be another overhyped concept — what I like to call the “blockchain moment.”
Remember when everyone claimed blockchain would revolutionize everything from finance to healthcare?
While it has found a few solid applications, much of the buzz fizzled out because the technology didn’t match the hype.
Agentic AI could follow the same path.
At myPizza bakery, we might invest in a system marketed as “agentic,” only to find it doesn’t really deliver more value than our existing tools.
The promises are big, but the technology might not be ready for prime time.
So, while agentic AI holds promise, it’s important to approach it with caution.
The technology has limitations, poses ethical risks, and could be more hype than reality — at least for now.
But understanding these challenges is the first step toward using AI responsibly and effectively.
With that, let’s move to the final part of our discussion: how we can strike the right balance when adopting agentic AI.
4. How to Approach Agentic AI Critically
As we wrap up, let’s talk about how we can approach agentic AI with a critical yet open mind.
It’s not about dismissing the technology outright or blindly believing the hype — it’s about asking the right questions and adopting a balanced perspective.
Ask the Right Questions
When evaluating agentic AI, start with these key questions:
1. Who benefits from agentic AI?
At myPizza bakery, for instance, if an AI vendor claims their system will revolutionize our operations, we need to ask:
Does this benefit the bakery, our customers, or just the vendor trying to sell their product?
Is it genuinely solving problems for us, or just creating flashy features we don’t need?
2. What are the measurable outcomes?
Instead of vague promises like “improved efficiency,” look for specific, measurable results.
Will it reduce ingredient wastage by 10%? Will it cut delivery times by 15 minutes?
At the bakery, we could test this by running a pilot program and comparing metrics like customer satisfaction and operational costs.
3. Is the technology solving real problems?
This is crucial. If the AI is helping us address bottlenecks — like scheduling delivery routes or managing seasonal demand — it’s valuable.
But if it’s only adding complexity without clear benefits, it’s not worth the investment.
Adopt a Balanced Perspective
It’s also important to maintain a realistic outlook.
1. Recognize incremental progress rather than revolutionary change.
Agentic AI may not completely transform our bakery overnight, but it might offer small, meaningful improvements — like better inventory management or smarter staffing schedules.
These incremental gains can add up over time.
2. Look for transparency: Be cautious of companies making grand claims about agentic AI.
At myPizza bakery, if a vendor refuses to explain how their system works or won’t provide examples of successful deployments, that’s a red flag.
On the other hand, vendors who share clear, detailed explanations and real-world case studies are worth considering.
By asking the right questions and staying grounded in reality, we can separate the hype from the value and make better decisions about adopting agentic AI.
At myPizza bakery, this means using technology that genuinely improves our operations, enhances customer experience, and aligns with our goals — not just chasing the latest buzzword.
With that, let’s move on to our conclusion and key takeaways.
Thank you all for being such an attentive reader.
So, let’s revisit the big question: is agentic AI a buzzword, hype, or a paradigm shift?
From what we’ve seen, the answer isn’t black and white.
Agentic AI has some exciting possibilities — it could help businesses like myPizza bakery run more efficiently, anticipate customer needs, and reduce human workload.
However, the technology is still in its infancy, with significant limitations and risks. And in some cases, it’s clearly overhyped by those eager to ride the AI wave.
Here’s my takeaway: Agentic AI is neither a complete game-changer nor just a fleeting fad.
It’s a technology with potential that’s still finding its footing.
The real question is how we use it — and whether we approach it with enough skepticism and responsibility.
As we move forward, I encourage all of you to stay informed and critically question bold claims about agentic AI.
When you hear someone say, “This AI can do everything,” ask: Who really benefits? What are the measurable outcomes? Is it solving a real problem or just creating more noise?
To close, let me leave you with a thought-provoking question:
Agentic AI may or may not be the next big thing, but are we prepared for the world it could create?
A world where our AI assistant at myPizza bakery might someday manage not only our inventory but also decide when to close the bakery for the day.
Are we ready to trust such systems with decisions that matter?
If you loved this story, please feel free to check my other articles on this topic here: https://ankit-rathi.github.io/data-ai-concepts/
Ankit Rathi is a data techie and weekend quantvestor. His interest lies primarily in building end-to-end data applications/products and making money in stock market using Quantvesting methodology.
