Not all AI is the same and we’re starting to see that more clearly than ever. Over the last year, tools like ChatGPT and DALL·E have taken over headlines with their ability to write, design, and code on command. They’re fast, impressive, and surprisingly creative. That’s the world of generative AI: systems that can produce content based on what you ask.
But a new kind of AI is now entering the picture. It’s not just answering questions or generating images. It’s thinking through tasks, using tools, setting goals, and even figuring out what to do next. This is called agentic AI, and it’s changing how we define what “smart” really means.
That’s why the conversation around agentic AI vs generative AI is gaining attention. If generative AI can create, and agentic AI can act, which one is actually smarter? And what does that mean for the future of AI in business, tech, and everyday life?
Let’s find out!
What is Generative AI?
Let’s say you ask a robot to draw a picture of a cat riding a skateboard, and it actually gives you a pretty cool image in a few seconds. Or imagine asking it to write a bedtime story, and it writes something that sounds like a real author. That’s generative AI in action. It’s a type of artificial intelligence that creates things. It can write, draw, make music, code, and even generate human-like conversations based on the instructions you give it.
The word “generative” comes from the idea of generating something new. It doesn’t just repeat what it’s seen. Instead, it learns patterns from a huge amount of information, like books, images, songs, or websites, and then uses those patterns to create new content that sounds or looks original.
One of the most popular examples is ChatGPT, made by OpenAI. You type in a question, and it gives you a full answer in seconds. Some people use it to help with homework. Others use it to write code, emails, blog posts, or even jokes. Another example is DALL·E, an AI that can turn your words into pictures. You could ask it for “a giraffe in a space suit on the moon,” and it would actually create that image.
Generative AI is everywhere now. It helps writers brainstorm. It helps developers write code faster. It helps marketers come up with social media captions. Even musicians use it to mix sounds or explore new styles.
But here’s something important to know! Generative AI is smart in its own way, but it’s not thinking for itself. It doesn’t understand what’s “true” or “right.” It’s really good at predicting what words or images should come next, based on patterns, kind of like the world’s most advanced autocomplete. It doesn’t have goals or memory of past conversations unless it’s specifically programmed to.
So, while it can create amazing stuff, it doesn’t actually know what it’s doing. It’s not planning. It’s not learning from its own mistakes. It’s not acting on its own unless someone tells it what to do. That’s where agentic AI (which we’ll talk about next) comes in.
Generative AI is like a super creative assistant who never gets tired. You give it the instructions, and it produces something cool. But it won’t take initiative or figure out what’s next on its own. It’s powerful, but it’s not truly “independent.” Still, it’s changing how we write, build, and create, and it’s only getting better.
Generative AI Stats
- Generative AI could become a $1.3 trillion market by 2032. (Bloomberg)
- 67% of IT leaders prioritize generative AI for their business within the next 18 months, with 33% considering it a top priority. (Salesforce)
- Around 45% of the U.S. population uses generative AI, with an estimated 115 to 180 million global daily active users across major platforms like ChatGPT, Gemini, and Copilot as of early 2025. (TechnoLlama)
- 68% of users utilize generative AI for asking questions. (AIPRM)
- 71% of companies are now using generative AI in at least one part of their business, and that number keeps rising fast. (McKinsey)
- 97% of leaders investing in AI, report positive ROI. (EY)
- 71% of images shared on social media globally are AI-generated in 2024. (Artsmart.ai)
- 30% of new smartphones to feature on-device GenAI .(Deloitte)
What is Agentic AI?
Imagine if the same AI that answered your questions could also decide what to do next, take steps on its own, use tools, and complete a whole task, without you giving it every instruction. That’s what agentic AI is all about. It’s not just reacting to you; it’s taking initiative like a digital assistant with a brain.
The word “agentic” comes from the word “agent,” which means someone (or something) that can act. So, agentic AI is artificial intelligence that acts like an agent. It has a goal, it remembers what it’s doing, and it can figure out how to complete a task step by step. Unlike generative AI, which needs a prompt every time, agentic AI can take a prompt and then get to work on its own.
Let’s say you want research on the best laptops under $1,000. With generative AI, you’d have to ask it for a list. Then maybe ask for comparisons. Then maybe ask which one is better for editing videos. You’re guiding it the whole way.
With agentic AI, you can say: “Find me the best laptop under $1,000 for video editing, compare the top 3 models, and write a summary with links.” It will break that big request into smaller tasks, do each one on its own, and return with the final answer already written. Some versions even go back to double-check if they missed something. That’s the agentic part.
You can think of it like this: generative AI is like a clever student waiting for your next question. Agentic AI is like a smart intern who understands your goal and figures out how to get there, step by step, without needing constant direction.
One early example of agentic AI is a tool called AutoGPT. When it launched, it caught everyone’s attention. You could give it a goal, like “Plan a week-long vacation to Tokyo under $2,000,” and it would open search tools, gather options, compare prices, and even suggest an itinerary, all on its own. It didn’t just answer your question; it tried to solve your problem.
Agentic AI systems usually include things like memory (so they don’t forget what they’re working on), planning (so they can break big tasks into smaller ones), and tool use (so they can access apps, search engines, or other software to get stuff done). Some use APIs, some open spreadsheets, some send emails, and all of this happens while they’re working in the background.
That’s what makes them so interesting. They don’t just think, they act.
Of course, agentic AI is still new. It doesn’t always get things right. Sometimes it repeats steps, gets stuck, or makes assumptions that don’t match reality. It also takes more setup and testing than simple prompt-based tools. But the big idea is clear: this is the next step in AI evolution. We’re moving from AI that talks, to AI that does.
Just like we don’t expect a hammer to build a house on its own, we haven’t expected AI to complete tasks without us. Agentic AI changes that. It’s not just helping us think — it’s helping us act faster, better, and with less hand-holding.
And that’s why so many companies are now exploring how to use agentic AI in their workflows, products, and everyday operations.
Key Features of Agentic AI
- Acts toward a goal with minimal human input
- Plans tasks by breaking them into steps
- Remembers progress and past actions
- Uses tools, apps, and APIs to complete tasks
- Can loop, retry, or adjust its steps along the way
- Works like a task agent, not just a chatbot
- Ideal for automating workflows or multi-step research
Agentic AI Stats
- The global agentic AI market size is expected to grow rapidly to USD 41.32 billion by 2030. (Mordor Intelligence)
- Autonomous vehicle penetration is expected to increase from 0.49% in 2024 to 12% by 2030, largely due to agentic AI advancements. (Mordor Intelligence)
- Agentic AI Will Autonomously Resolve 80% of Common Customer Service Issues Without Human Intervention by 2029. (Gartner)
- Experts predict that by 2027, 50% of the companies that currently use a generative AI model will have launched agentic AI pilots or at least proof of concept. (Deloitte)
- Multiple industry forecasts predict that AI agents will automate up to 70% of tasks around the office throughout the next decade. (Forbes)
- 29% of organizations are already using agentic AI, with 44% planning to implement it within the next year. (LangChain)
- As of 2025, more than 20% of the global market value for agentic AI was accounted for in North America alone. (Emergen Research)
- 25% of enterprises will deploy AI agents this year. (Deloitte)
What is the Difference Between Generative AI and Agentic AI?
At first glance, agentic AI and generative AI might seem similar as both respond to prompts, use large language models, and can impress you with their speed. But the way they work under the hood is very different. Think of it like this: generative AI is like a really smart artist, while agentic AI is more like a project manager who also knows how to paint.
Here’s a simple comparison of agentic AI vs Generative AI:
What this really means is that generative AI is great at helping you get things started, such as writing a draft or generating ideas. But agentic AI is better at getting things finished. It knows how to stay on track, move from one step to the next, and make decisions on its own.
It’s kind of like the difference between asking someone, “Can you help me write a list of restaurants?” vs “Can you find the best one, make a reservation, and text me the details?” The first is generative. The second is agentic.
Both are powerful in their own way, but if you’re looking to build systems that think ahead, handle tasks, or make your workflows more autonomous, agentic AI is the one doing more than just responding. It’s actively working for you.
Which One is Smarter?
When comparing agentic AI and generative AI, determining which is “smarter” depends on the context and the specific tasks at hand.
Generative AI excels at creating content. It’s like a talented artist or writer who can produce poems, stories, images, or music based on the patterns it has learned from vast amounts of data. For instance, tools like ChatGPT can generate human-like text, while DALL·E can create images from textual descriptions. However, generative AI typically requires prompts to produce output and doesn’t possess the ability to make decisions or take actions beyond its content generation capabilities.
Agentic AI, on the other hand, is designed to act autonomously. It’s akin to a skilled assistant who not only understands tasks but can also plan, make decisions, and execute actions without constant supervision. For example, agentic AI can manage complex workflows, adapt to new situations, and make decisions based on real-time data. This makes it particularly useful in scenarios requiring dynamic decision-making and adaptability, such as autonomous vehicles or real-time customer service bots.
Recent studies highlight the growing capabilities of agentic AI. According to a survey by Cornell University, agentic AI represents a significant advancement in AI evolution, offering stronger reasoning and interaction capabilities that enable more autonomous behavior to tackle complex tasks.
In practical applications, companies are increasingly integrating agentic AI into their operations. For instance, Salesforce has reported that its agentic AI handles 66% of website inquiries and provides proactive customer recommendations, showcasing its effectiveness in real-world tasks.
So, while generative AI is unparalleled in content creation, agentic AI offers superior capabilities in autonomous decision-making and task execution. The choice between the two depends on the specific needs:
- Agentic AI is “smarter” in terms of autonomous reasoning, decision-making, and task execution. It can think, plan, and act independently, making it suitable for complex, dynamic environments requiring proactive problem-solving.
- Generative AI is “smarter” in creative content generation and rapid production of human-like outputs but lacks autonomy and strategic decision-making capabilities.
Real-World Applications
Generative AI Use Cases:
Marketing & Creative Content
Generative AI helps brands write product descriptions, generate social media posts, and even create ad visuals. Coca-Cola and Nestlé have used it for campaign ideation, while agencies are using tools like Midjourney and Jasper to scale up content.
Customer Service
Generative models are behind many of today’s chatbots and virtual assistants. Companies like Accenture and Verizon use GenAI-powered bots for real-time customer support, helping deflect low-priority tickets and reduce wait times.
Legal and Compliance
Law firms and corporate legal teams use generative AI to draft contracts, summarize legal documents, and analyze risks. Tools like Harvey.ai are being adopted by global firms like Allen & Overy.
Healthcare
Generative AI assists with summarizing patient data, drafting reports, and even helping radiologists by generating clinical notes from voice inputs. Google Health is piloting such features in real-world settings.
Coding and Software Development
Developers use GitHub Copilot and Amazon CodeWhisperer to generate code, detect bugs, and speed up deployments. This has cut development time by nearly 55%, according to GitHub’s internal study.
Agentic AI Use Cases:
IT Operations
Companies like Moveworks use agentic AI to manage access requests, reset passwords, and troubleshoot outages automatically, without needing a human to guide each step.
Supply Chain & Logistics
Agentic AI agents are being deployed to automate inventory tracking, reroute shipments in real time, and forecast demand. Walmart and Amazon have both explored autonomous systems for warehouse optimization.
Finance & Fintech
Agentic systems assist in fraud detection, customer onboarding, and portfolio analysis. For instance, Klarna uses AI agents to answer 2/3 of customer queries without human intervention, reportedly reducing support costs by 80%.
Research & Intelligence
AI agents can act like research assistants, browsing the web, collecting sources, summarizing documents, and returning insights. Products like AutoGPT and AgentGPT are used for tasks like competitive analysis, job searches, and report generation.
Enterprise Assistants
In Salesforce’s Einstein Copilot and Microsoft’s Copilot for 365, agentic logic is being used to perform full workflows, like updating records, generating reports, or sending follow-up emails after meetings, all triggered by simple voice or text commands.
Hybrid Approach Use Cases:
Microsoft Copilot
Generative AI writes emails, drafts meeting summaries, and explains documents in tools like Word and Outlook. Agentic AI takes action. it checks your calendar, pulls related files, suggests follow-ups, and schedules meetings, all based on your current task.
Salesforce Einstein Copilot
Generative AI creates support replies, sales messages, and document summaries. Agentic AI pulls live customer data, updates CRM records, recommends next steps, and routes tasks to the right team, without extra clicks.
Klarna’s AI Customer Service Agent
Generative AI chats with users in natural, human-like language. Agentic AI understands intent, checks order details, processes refunds, and closes tickets, handling two-thirds of customer requests end-to-end.
Notion AI
Generative AI helps you write notes, summaries, and action points. Agentic AI turns those into tasks, assigns them, and can even notify your team; moving from writing to managing workflows automatically.
GitHub Copilot + DevOps Tools
Generative AI writes and explains code in real time. Agentic AI identifies bugs, writes tests, tracks issues, and even helps launch updates through automated pipelines, helping manage the build from start to finish.
The Future Signals a New Convergence in AI
We’re entering a new phase of AI where creative tools and intelligent agents are starting to merge. For the last few years, generative AI has been in the spotlight. But now, more AI systems are learning how to act, not just create.
Today, we’re already seeing examples where these two types of AI work together. Tools like Microsoft Copilot or Salesforce’s Einstein Copilot don’t just help you write emails, they can pull in customer info, update your files, and even suggest what to do next. Klarna’s AI doesn’t just chat with users. It reads the request, takes action, and solves the problem.
This blending of creative and action-oriented AI is expected to grow fast. According to Deloitte, about a quarter of companies using generative AI today are already testing agentic features. By 2027, that number could double. More businesses want AI that doesn’t just sound smart, they want it to do smart things too.
But with that progress comes some big questions. If AI starts making decisions on its own, who’s responsible when something goes wrong? Can these systems explain how they reached a decision? These are problems researchers and developers are now racing to solve.
Still, one thing is clear: the future of AI isn’t about choosing between generative or agentic. It’s about combining the two. When creativity meets action, AI becomes more useful, more human-like, and much more powerful.
How Bajco Technologies Can Help
At Bajco Technologies, we help businesses use AI and machine learning to work smarter. Our team builds custom solutions that automate tasks, analyze data, and deliver insights to improve decision-making.
Whether it’s creating predictive models, integrating AI into your existing systems, or offering strategic guidance, we focus on practical tools that make your operations more efficient and effective.
Curious how it could work for you? Book a demo today and let’s explore what’s possible.
FAQs
What’s the main difference between agentic AI and generative AI?
Generative AI creates content. Agentic AI takes action toward a goal.
Can generative AI become agentic?
Generative AI can become agentic when combined with memory, planning, and tool-use capabilities.
Which type of AI is better for businesses?
It depends. Generative AI is great for content creation, while agentic AI excels at task automation. Many businesses now use a hybrid approach to get the best of both.
Is agentic AI the future?
Agentic AI is a major part of AI’s future, especially as businesses seek systems that can think, plan, and act. Its real power lies in combining autonomy with generative tools.
Are AI agents and agentic AI the same?
AI agents and agentic AI are closely related but not exactly the same. An AI agent is any system that can do tasks for you. Agentic AI is a smarter kind of agent that can plan, remember, and make decisions on its own.