What is Generative AI and Agentic AI
When most marketers think about artificial intelligence, they picture generative tools like ChatGPT, Gemini, or MidJourney. Tools that create blogs, images, or even code. But marketing is moving into a new phase of AI where it’s not just about creating content, it’s about executing campaigns. That’s where agentic AI comes in.
To stay competitive, marketers need to understand generative AI, agentic AI, and most importantly, the difference between them. This isn’t a technical detail. It’s the foundation for faster, smarter, and more scalable marketing.
What Is Generative AI?
Generative AI is the type of AI most people already know. It’s designed to create new content like text, images, videos, or code, by learning patterns from existing data. Using that knowledge, generative AI produces new, contextually relevant content in response to your instructions or prompts. Instead of “understanding” the way humans do, it predicts what’s most likely to come next and generates content that feels original.
What it’s best at:
- Producing text, images, and code
- Powering chatbots and virtual assistants
- Scaling creative ideas quickly
Common uses:
- Blog writing
- Ad visuals
- Automated customer responses
Popular tools:
- ChatGPT
- Gemini
- Claude
- DALL·E
- MidJourney
- Jasper
- GitHu
- Copilot
Generative AI is powerful and has completely reshaped the marketing landscape, but it has one key limitation: it’s reactive. It waits for a prompt, delivers an output, and stops there. It doesn’t decide what needs to happen next. That’s where agentic AI comes in.
What Is Agentic AI?
Agentic AI is designed to operate more autonomously. Instead of simply responding to prompts, it can set goals, plan tasks, and take action to achieve results. Think of it as AI that can plan and execute: it breaks big objectives into smaller steps, figures out the best approach, and carries out tasks, usually without needing constant human input.
What it’s best at:
- Planning and prioritizing actions
- Executing across multiple steps without constant oversight
- Adapting and optimizing in real time
Examples in practice:
- An AI content strategist that finds trending topics, researches keywords, builds a content calendar, and drafts article outlines
- An AI sales agent that engages inbound leads, scores their fit, and books meetings once they qualify
- A social media agent that analyzes real-time audience data and automatically schedules posts for the moments with the highest engagement potential
Where generative AI is reactive, agentic AI is proactive. It takes initiative to drive results.
Agentic AI vs Generative AI: What’s the Difference?
| Category | Generative AI | Agentic AI |
| Autonomy | Low; reactive to prompts | High; proactive, and goal-driven |
| Function | Creates content (text, images, code) | Plans, executes, and optimizes tasks |
| Use case | Blogs, ads, images, chatbots | Campaign execution, bidding, and personalization |
| Outcome | Scalable content production | Scalable execution and optimization |
In short: Generative AI is the “what.” Agentic AI is the “how.”
Why Marketers Should Care About This Distinction
Generative AI is extremely useful for scaling content quickly, but it stops at creation. It doesn’t know what to do with that content: How to share it, where to put it, or how to improve it once it’s live. That’s where agentic AI takes over. It takes the next steps: delivering the right content to the right channels, learning what works in real time, and making improvements automatically so your team doesn’t have to.
The distinction matters because the future of AI in marketing isn’t just about generating ideas, it’s about deciding what to do next, executing it, and improving it.
WITHIN’s POV: Designing for Agentic Execution
At WITHIN, we see generative AI as a valuable starting point but it’s just that: a starting point. The real opportunity comes when AI can not only create but also act, adapt, and improve inside performance-driven frameworks.
That’s why we design agentic workflows that don’t just generate content, but can set goals, run processes end-to-end, adapt as results come in, and keep humans involved where it matters. For the brands we partner with, this means faster execution, smarter use of resources, and measurable performance improvements without having to manage complex AI systems themselves.
Our focus is on operationalizing AI across the full funnel. That means:
- Identifying opportunities for automation by reviewing workflows and pinpointing repetitive tasks that AI can handle.
- Connecting systems and data sources so AI can act across channels instead of working in silos.
- Keeping humans in the loop to guide decisions, validate results, and make sure outcomes align with strategy.
WITHIN’s advantage is that we can tie AI directly to media, creative, and analytics, turning it from a helpful tool into a driver of real business outcomes.
Examples of Generative and Agentic AI in Marketing
We’ve helped brands use AI to accelerate creative production and strengthen campaign performance.
- Touchland: To support the launch of Touchland’s Body Mist, we used AI-driven workflows to produce 70+ assets in just four weeks, including 3D visuals that brought each scent to life.
- Nuts.com: We combined AI-powered workflows with traditional post-production to efficiently deliver 900+ ads, testing copy and visuals at scale.
These examples show how AI can be applied not just for efficiency, but to produce large-scale creative, accelerate go-to-market timelines, and improve campaign performance.
How to Start Applying Agentic Thinking
Adopting agentic AI doesn’t require an all-or-nothing mindset. The best place to start is by shifting the question from “what can AI create for me?” to “what can AI do for me?” Start by:
- Identifying repetitive tasks that can be automated: Look for areas where teams spend time on manual work such as reporting, campaign monitoring, or customer responses.
- Testing low-risk automations: Start by piloting agentic tools in controlled scenarios such as budget pacing, A/B test management, or email send-time optimization.
- Ensuring systems are connected: Agentic AI is most effective when tools like CRM, analytics, media platforms, and creative workflows can “talk” to each other.
- Build a human feedback loop: Keep people involved by setting checkpoints where teams can guide, validate, and refine outcomes.
Key Takeaways
- Generative AI = content creation.
- Agentic AI = execution and optimization.
By combining the two, WITHIN helps brands move faster, work smarter, and turn ideas into measurable results.
Ready to start leveraging AI in our workflows? Let’s talk.
Author
Michael Choi, Head of AI