I think it’s safe to say many of us are now familiar with generative AI tools like ChatGPT or Midjourney, There’s another emerging class of AI with even more transformative potential: agentic AI.
Agentic AI – I promise you now, this is a term of the coming weeks and months you’re going to get very used to hearing (read: probably get really bored of reading!) However, understanding the distinction between these two approaches is critical for businesses, developers, and everyday users as we shape the future of AI collaboration.
Recap: What Actually Is Generative AI?
Generative AI refers to systems that create (read: generate) content—text, images, code, music—based on user prompts. These systems are reactive: they wait for human input, process it using patterns learned from massive datasets, and then produce a result. They’re incredibly useful for tasks like:
- Writing emails
- Designing visuals
- Generating background music
- Creating summaries or code snippets
But here’s the key: generative AI stops at generation. It doesn’t take initiative. It doesn’t act unless instructed. It’s like the world’s worst co-worker who you have to micro-manage. With generative AI the human remains firmly in the driver’s seat, curating and directing the output every step of the way.
So, What Is Agentic AI?
Agentic AI takes things a little further. It is proactive, designed to pursue goals and make decisions autonomously. While it may begin with a prompt, just like generative AI, it can then:
- Perceive its environment
- Decide on actions
- Execute those actions
- Learn from the results
- Iterate, all with minimal human input
To make this more realistic, let’s use the example of a personal shopping agent. Once you tell it what you want to buy (say, a new paddling pool for the children), it could start by searching across retailers who stock paddling pools. The next step could be to monitor price changes, making sure you get the best splashing bargain! Then it could handle the operational side and sort out the checkout and even schedule delivery around school pick-up! Now I don’t know if it’s just me, but as a busy parent, this sounds like music to my ears. Taking boring admin away from my life, leaving me more time to enjoy said paddling pool.
That’s a multi-step process, managed largely without human involvement.
A Shared Backbone – Large Language Models
Both generative and agentic systems are powered by large language models (LLMs)—the same kind behind tools like GPT-4. These models not only generate content but also enable something more advanced: chain-of-thought reasoning. This allows agentic systems to internally “think through” problems, breaking complex tasks into logical steps. For example, an agent tasked with planning a party might reason:
“First, I need to understand the size, budget, and requirements. Then, I’ll research venues. Next, I’ll check availability…”
This is AI simulating strategic planning, not just producing isolated outputs.
From Tools to Teammates
I think it’s unlikely that we’ll rely solely on either generative or agentic AI in the near future. The most impactful systems will be hybrid collaborators—capable of both creative exploration and goal-oriented action. Imagine an agent that not only outlines a product roadmap based on your business goals, but also coordinates with your team’s calendars, drafts sprint plans, assigns tasks, and follows up on blockers—proactively moving the project forward while you focus on strategic priorities.
The future isn’t just AI that responds—it’s AI that partners.
Author
She is also an award-winning content creator, podcast host, event moderator, and speaker, with multiple honors and recognitions, including the CN 30underThirty in 2022. She leverages her expertise and passion for data and infosec to produce and host industry-leading content, moderate large-scale events, and spearhead communities that foster knowledge sharing and collaboration among professionals and leaders.
In addition, Catherine is an instructor at the University of British Columbia's Sauder Business School, where she teaches the Marketing Intelligence and Performance Optimization module for their Data and Marketing Analytics Course. She enjoys sharing her insights and best practices with the next generation of data and marketing analysts.
Catherine holds a Bachelor of Economic and Social Studies from Cardiff University, with a focus on sociology. She is committed to promoting diversity, inclusion, and accessibility in the industry, and is a vocal ally and advocate. Outside of work, she loves gardening and spending time with her partner, son and two Pomeranians.
