The Big Shift: The Sunday Prompt

Welcome to The Sunday Prompt.

Each week, we take a breather from the firehose of AI news to focus on a single storyline, technology concept, or applied skill—explained in plain language. The goal? To build fluency so we can better parse the news, separate hype from reality, and approach AI in our own work with confidence.

For our inaugural edition, we’re tackling one of the most talked-about and most misunderstood AI concepts today: Agentic AI.

Everyone from tech CEOs to AI “influencers” is hyping it up. Companies are racing to brand their AI as "agentic." But what does it actually mean? Is it real yet? And how does it differ from AI agents, which we’ve been hearing about just as much?

Let’s break it down.

Agentic AI: Everywhere in Name, Nowhere in Reality

If you’ve been following AI news, you’ve probably heard Agentic AI mentioned constantly over the past few months.

NVIDIA’s CEO, Jensen Huang, has been making bold predictions about an AI workforce made up of autonomous, agentic employees—AI that doesn’t just assist humans but takes on roles within organizations. Meanwhile, Salesforce has rebranded itself as an Agentic AI platform, making a bold bet on the rise and eventual ubiquity of AI workers. And across the industry, startups are scrambling to position themselves as pioneers in building this next generation of AI workers.

But what exactly is Agentic AI? Can you use it today? And are AI agents and Agentic AI the same thing?

What Is Agentic AI?

Agentic AI refers to AI systems that set their own goals, plan how to achieve them, and execute tasks autonomously. Unlike traditional AI models that require specific prompts or predefined workflows, Agentic AI can act independently.

A true Agentic AI system should be able to:

✅ Define its own objectives – It doesn’t need a human to tell it what to do next; it determines what needs to be done.
✅ Plan and execute – It doesn’t just react to requests; it breaks complex goals into steps and completes them.
✅ Adapt and learn continuously – It refines its own strategies over time instead of just following scripts.
✅ Coordinate multiple AI systems – It integrates different tools to accomplish goals across domains.

The idea is that Agentic AI could function like an autonomous worker, handling workflows, making decisions, and optimizing processes without requiring constant human input.

A Hypothetical Example: Agentic AI as a Digital Marketing Employee

Jensen Huang predicts that some digital marketing roles will one day be filled by AI-based digital workers. While this is still hypothetical, a true Agentic AI would go beyond assisting with tasks—it would operate like an independent employee, setting and executing its own marketing strategy.

Here’s how it could work:

1️⃣ Identifies a Goal – The AI detects that online orders are down and determines it needs to increase sales.
2️⃣ Develops a Strategy – It decides to launch a promotional campaign, choosing social media ads, email marketing, and discount offers.
3️⃣ Creates and Deploys Content – It writes ad copy, designs images, and schedules posts without human input.
4️⃣ Monitors Performance – It tracks which ads, emails, and discounts are driving sales and which aren’t.
5️⃣ Optimizes Over Time – It reallocates the budget, adjusts messaging, and refines targeting based on real-time results—just like a human marketer would.

Again, this is hypothetical—this level of autonomy doesn’t exist yet.

Read on for more on where things stand today.

Where Things Get Confusing: AI Agents vs. Agentic AI

Now that we know what Agentic AI is supposed to be, why is the term being thrown around so loosely—and why does it matter?

Agentic AI and AI agents are not the same thing!

Right now, a lot of companies and the media are conflating AI agents with Agentic AI. AI assistants, automation tools, and decision-making systems are being branded as agentic, even though they don’t actually meet the definition. So, what’s the difference?

A good rule of thumb:

✅ All Agentic AI are AI agents, but not all AI agents are Agentic AI.

The key difference? Autonomy and goal-setting.

AI Agents (Not Agentic AI)

An AI agent is any system that can make decisions and take actions within a defined scope. These systems react to inputs and follow predefined goals, but they don’t set their own objectives.

AI agents are everywhere—you probably interact with them daily, often without even realizing it.

Examples of AI Agents (Not Agentic AI)

🔹 Customer service chatbots – AI agents can troubleshoot and respond but don’t create their own strategies for improving service.
🔹 AI-powered spam filters – AI detects and blocks phishing attempts but isn’t making independent security strategies.
🔹 Tesla Autopilot – The AI decides how to drive based on real-time conditions but follows programmed safety rules.
🔹 Fraud detection in banking – AI flags unusual transactions and blocks suspicious activity.

Agentic AI takes things a step further, proactively setting its own goals and determining how to achieve them.

Right now, most so-called "Agentic AI" systems are still just advanced AI agents. They have some autonomy but cannot operate across different business functions without human oversight and intervention.

So… Are We Agentic Yet, or What?

Not even close. Here’s what’s holding it back:

1. Data Silos and API Limitations

🔹 AI can’t freely access most business, government, or proprietary datasets, limiting its ability to self-direct.
🔹 Many software platforms restrict AI access, preventing seamless automation.

2. AI Still Lacks Real-World Reasoning

🔹 AI struggles with ambiguity and multi-step reasoning.
🔹 It hallucinates facts, making autonomous decision-making risky.

3. Memory and Context Are Limited

🔹 Most AI forgets everything after each interaction, making persistent learning difficult.

4. Safety, Alignment, and Control

🔹 The more autonomous AI becomes, the harder it is to control and align with human intentions.

5. Compute Costs and Infrastructure Limits

🔹 Building true Agentic AI is expensive, requiring massive computational power.

When Might True Agentic AI Arrive?

It’s hard to say when Agentic AI will truly arrive, but we can make some educated guesses about when and how it might start to take shape.

🔹 Short-term (1–3 years): Expect better AI agents and a lot more of them, improved memory, and better automation.
🔹 Medium-term (3–5 years): Limited Agentic AI will emerge in research and cybersecurity. Enterprise applications like ERP and CRM—where AI can operate within structured, controlled environments—will likely be an early testing ground for agentic capabilities in business workflows.
🔹 Long-term (5+ years): Truly general Agentic AI—able to operate across domains and autonomously manage complex systems—is still far off.

The biggest barrier right now isn’t the technology—it’s access to data and the ability to integrate AI across platforms. Until those issues are solved, we’ll remain in a more aspirational phase of Agentic AI. Expect to see lots of advanced AI agents but not fully autonomous Agentic AI.

Final Thought: AI Agents Are Everywhere—Agentic AI Is Still Nowhere (Yet)

✅ AI Agents: We’re already interacting with them every day.
🚧 Agentic AI: Still in the very early stages of development.

Right now, Agentic AI is everywhere in name but nowhere in reality. Expect plenty of "agentic" branding, but keep in mind that most of what’s being called Agentic AI today is just a fancied-up AI agent—or a really cool demo.

See you next Sunday!

Extra Credit: What About ChatGPT, Claude, and Gemini?

Are they agents? No. Are they agentic? Also no.

These are Generative AI models, meaning they create content (text, images, music, code)—but they don’t take independent actions.

Some companies are trying to give Generative AI agent-like capabilities, but on its own, it’s neither an agent nor agentic. It produces information but doesn’t execute tasks. That’s what separates it from AI agents and Agentic AI.