Think about the conversations swirling through boardrooms and living rooms alike: Where is artificial intelligence truly taking us, and how do we separate real progress from buzzword overload? The conversation around AI has moved beyond speculation. Our devices and apps are infused with intelligence, but how often do we pause to ask? Are we using tools that merely react to us, or are we engaging with systems that can set and accomplish tasks independently? That distinction matters more than ever, yet it is often muddled in industry jargon and ambitious marketing.
A recent video presentation by Martin Keen at IBM sets the record straight, offering a much-needed roadmap through the crowded landscape of AI. By dissecting the differences between Generative AI and Agentic AI, Keen provides insight into how each technology is already shaping the workplace and hints at where the most exciting shifts are still to come. Let’s break down the content of the video step by step. step.
Generative AI: Delivering output when you ask
Generative AI has captured the world’s attention with its ability to produce copy, draft professional emails, and turn basic prompts into eye-catching graphics almost instantly. The premise is direct: These systems generate text, images, audio, or code by finding patterns within enormous training data. Instead of initiating actions, they excel at responding to user direction thanks to prediction capabilities built on advanced large language models (LLMs). Here’s how it works. A user gives a prompt, and within seconds, the tool returns a detailed answer, a marketing plan, or even a piece of fanfiction. This impressive speed and flexibility are powered by LLMs that have absorbed huge libraries of language or imagery.
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It is important to remember, however, that Generative AI systems offer no autonomy. Unless a user feeds in another prompt, they do nothing. They are producers, not planners or actors. This makes them valuable for brainstorming, designing, or expanding creative work. At the same time, these systems are not suitable partners for tasks that require ongoing management or self-direction. The technology is already widely used. YouTubers use Generative AI to review scripts or brainstorm thumbnail designs. Marketing teams might use it to produce several versions of ad copy quickly. Human review and refinement are always necessary before any output reaches its final form, with the AI serving as a starting point rather than a finisher.
Agentic AI: From following instructions to owning the task
Agentic AI takes the conversation to a different level. Where Generative AI is limited to responding, Agentic AI proactively identifies what must be done, charts out the path, and carries out a sequence of actions to meet a broader goal. Give Agentic AI a project, and it will assess the situation, plan the necessary steps, execute them, and tweak its approach as it learns from each outcome.
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This style of AI is well suited to cases where ongoing adjustments and multi-step reasoning are in play. Consider a personal shopping assistant: It does not just suggest products. Instead, it tracks prices, monitors for deals, completes purchases, and arranges delivery with minimal intervention. For event planning, Agentic AI tools can outline all required tasks, allocate resources, and oversee completion, pausing only for key approvals. A key enabler here is chain-of-thought reasoning, which allows the system to break up large tasks, analyze each piece, and reflect on progress as it moves forward. The end result is a technology that looks more like a project manager than a copywriter.
Both powered by LLMs at the core
Despite the clear behavioral differences, Generative and Agentic AI often share the same core technology. LLMs serve as the engine for natural language processing and pattern recognition, whether generating a sonnet, managing a chatbot conversation, or handling complex reasoning in an Agentic system. This foundation reminds us that advances in data training and language modeling remain central to every headline-grabbing application of AI today.
The future likely involves teamwork
Many experts believe that the next leap in AI will involve seamless collaboration between Generative and Agentic systems. Consider a scenario where a Generative AI proposes a marketing strategy, and an Agentic AI autonomously handles the scheduling, task tracking, and resource allocation required to make that campaign happen. Such collaborations are becoming a reality in early projects across tech and business.
Before you go: From Sentience to LLMs – How Language Unleashes Consciousness and Intelligence
This model allows machines to relieve us of repetitive, rule-based chores. At the same time, humans keep oversight, ensure context, and offer the real-world wisdom that no model can yet match. Solutions built on this principle can unlock more efficient workflows while protecting the integrity and nuance of human decision-making.
Why you should be paying attention
Choosing the right tools is about more than understanding the latest programming breakthrough. Generative and Agentic AI are redefining what it means to partner with intelligent systems in professional and creative settings alike. For anyone tracking the evolving potential of AI, realizing the strengths and weaknesses of both approaches is not just technical know-how. It could shape your next competitive edge. So what comes next? As these capabilities continue to blend, we are likely to see teams that put both types of AI to work, one responsible for generating ideas and options, the other managing execution and adaptation. That future does not belong to science fiction.
It is already on display in companies deploying the latest combinations of these technologies to tackle real business and creative challenges. Want a direct look at how these ideas translate into practice? Martin Keen explains the concepts with clarity and depth in his IBM Technology video. Watching is a valuable step for anyone who wants to thrive in tomorrow’s AI-driven workplace.
Photo credit: The feature image is symbolic and has been done by Christopher Isak with Midjourney for TechAcute.
