Agentic workflows: 7 marketing examples for 2026

For the past few years, using AI at work has mostly looked like this:

You ask → AI answers → you take it from there.

You ask for a LinkedIn post. You get a LinkedIn post.

You ask for keyword ideas. You get a list of keywords.

Useful? Absolutely.

But you are still responsible for knowing what you want, as well as connecting all the steps.

That is where agentic workflows come in.

Instead of using AI for one isolated task, an agentic workflow can move through multiple steps toward a larger goal. It can analyze information, decide what needs to happen next, use different tools or specialized AI agents and pass information between them.

For marketing teams, that could mean going from: “Write me a blog post.”

To: Research the topic → identify the angle → create the blog → turn it into social posts → create the newsletter → prepare everything for publishing.

That is a very different way of working with AI. So, what exactly is an agentic workflow, and what could one look like in marketing? Let's break it down.

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What is an agentic workflow?

An agentic workflow is an AI-driven process in which one or more AI agents work through multiple steps to achieve a goal.

Rather than waiting for a person to tell the AI exactly what to do at every stage, the system has more autonomy to determine the next action.

Google Cloud describes agentic workflows as dynamic processes in which AI agents can reason, plan and use external tools to carry out multi-step tasks. IBM similarly distinguishes them from traditional automation because they can adapt their actions instead of simply following the same predefined path every time.

The easiest way to understand the difference is to compare 3 common approaches.

1.Traditional automation

Trigger → predefined action → predefined action → result

For example:

Someone submits a form → add them to CRM → send predefined email.

The steps are already decided.

2.Generative AI

Prompt → AI generates output → human decides what to do next.

For example:

“Write an email announcing our new feature.”

AI writes the email, but you remain responsible for everything that happens before and after.

3.Agentic workflow

Goal → AI analyzes what is needed → tasks are completed → information is passed between steps → workflow progresses toward the result.

The key difference is not simply that AI is involved.

It is that AI becomes part of the workflow itself.

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What makes a workflow “agentic”?

A truly agentic system usually involves some combination of:

• A goal: What needs to be achieved?
• Reasoning: What should happen next?
• Planning: Which steps are required?
• Specialized agents: Which AI is best suited to each task?
• Tools and data: What information or systems need to be accessed?
• Context: What should carry from one step into the next?
• Actions: What can the system actually execute?
• Feedback: Does something need to be adjusted based on what happened?

That doesn't necessarily mean humans disappear from the process. For many business workflows, the best setup still includes human approval at important moments. The point is to remove the constant manual coordination between every small step. And marketing happens to contain a lot of those steps.

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Why agentic workflows matter for marketing

Marketing rarely consists of isolated tasks. Take something as simple as publishing a blog.

Before the blog exists, someone might need to research the topic, find relevant keywords, understand search intent, choose an angle, collect supporting information, write the article, create the meta title and description.

Then, after publishing, someone might turn it into a LinkedIn post, an Instagram post, a newsletter, a graphic or a short video.

Suddenly, “write a blog” is actually a workflow containing 10+ smaller tasks.

AI has already made many of those individual tasks faster.

Agentic workflows address the next problem: connecting them.

McKinsey describes this shift in marketing as moving toward AI-enabled workflows that integrate insights, content, commerce and performance rather than treating marketing as a collection of separate activities.

For smaller teams, the benefit can be even more obvious.

You may not have a researcher, SEO specialist, copywriter, social media manager and designer sitting next to you. But you still have all of those jobs to do.

Here are seven examples of what agentic workflows can look like in practice.

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1. The content campaign workflow

Imagine you want to create a campaign around one topic.

Normally, you might research the topic first, open another tool to write the blog, use another chat for LinkedIn, rewrite the same idea for Instagram and then build a newsletter from scratch.

An agentic content workflow could look more like:

Topic → research → content angle → blog → social posts → newsletter → publishing

Different specialized AI agents could handle different parts of the process while working from the same original context. Instead of asking one AI to pretend to be everything at once, you create a workflow around specialized roles.

This is also one of the ideas behind Whaaat AI: different marketing agents specialize in individual tasks while being able to work from shared context. A single topic can therefore move across different marketing formats without restarting from zero every time.

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2. The SEO content workflow

SEO content is another good example because writing the article is only one part of the job.

A more complete workflow might be:

Keyword → search intent → SERP research → angle → outline → article → SEO optimization → meta data → distribution

For example, you could start with: “We want to rank for agentic workflows.”

The first job isn't necessarily to start writing.

An AI workflow could first determine:

• What are people searching for?
• What type of pages currently rank?
• Do searchers want a definition, examples, tools or instructions?
• What questions appear repeatedly?
• Where might there be a gap?

Only then does the writing stage begin. Once the article is created, another agent could optimize its title and meta description, while additional agents repurpose the same research for other channels.

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3. The product launch workflow

Launching something usually creates a surprising amount of marketing work. You need positioning, messaging, a launch anouncement, social posts, emails, visuals, PR. Maybe even supporting educational content.

An agentic product launch workflow could start from one core brief:

Product information → audience → positioning → key messages → launch assets → channel adaptations → publishing

For example:

You upload information about a new feature. A research or strategy agent identifies the strongest benefits. A landing page agent creates the website messaging. A LinkedIn agent prepares the founder announcement. An Instagram agent adapts the launch for Instagram. An email agent writes the customer announcement. A graphic design agent creates supporting visuals.

They are separate tasks, but they belong to the same goal. Agentic workflows are interesting because those tasks can become connected parts of the same process.

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4. The content repurposing workflow

Some of the best content ideas already exist somewhere else.

Inside:

• A YouTube interview
• A TikTok
• An Instagram Reel
• A webinar
• A presentation
• A document
• A customer conversation
• An old blog post

The traditional process might involve watching the video, taking notes, finding the relevant sections, deciding what can be reused and then manually creating new content from it.

An AI-powered workflow can shorten that dramatically.

For example:

Video → transcript → key insights → content angles → blog → social content

You might find a useful industry interview and use it as source material for an original article discussing the topic from your company's perspective.

At Whaaat AI, for example, users can provide YouTube or Instagram links in chat so the content can be transcribed and used as source material by different marketing agents. The valuable part can happen after the information becomes available.

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5. The multi-platform social media workflow

One of the easiest mistakes to make with AI is generating one post and copying it everywhere.

Each platform has different expectations, formats and audiences.

A better workflow is:

Core idea → shared message → platform-specific agents → platform-native content

For example:

You give the system one announcement: “We're launching a new analytics feature. The main benefit is helping small teams understand which content is actually working.”

From that same brief: A LinkedIn agent might create an insight-led founder post. An Instagram agent might turn it into a shorter caption with a visual hook. An X agent might create a concise post or thread.

This is where multi-agent workflows become especially useful: shared context without identical output.

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6. The research-to-content workflow

One of the biggest opportunities for agentic AI has nothing to do with writing faster. It is reducing the work required before writing begins.

Consider how much time gets spent opening tabs, scanning articles, collecting statistics, looking through competitor pages and figuring out what is actually worth saying.

A research-to-content workflow could look like:

Question → web research → source evaluation → useful findings → angle → content

A marketing team could use this for:

• Industry trends
• Competitor research
• Thought leadership
• SEO articles
• Campaign concepts
• Product positioning
• Newsletter topics
• Social media commentary

The AI is helping build the information base first and that can make the output considerably more useful.

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7. The always-on content workflow

The most advanced version goes beyond requesting individual campaigns altogether. Instead, parts of marketing become ongoing systems.

For example:

Content plan → creation → review → approval → scheduling → publishing → performance → next content decision

An agent might identify upcoming content opportunities. Another prepares the assets. The marketer approves them. The content gets scheduled.

Performance information comes back into the system. Future recommendations change based on what happened.

That is much closer to the long-term idea behind agentic marketing: AI systems that help coordinate ongoing work.

Research into agentic marketing is already moving in this direction. Current examples include workflows that connect planning, creative production, analysis and execution rather than stopping once an AI has produced a piece of content.

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Agentic workflows vs. marketing automation

At this point you might be thinking: Isn't this just automation?

Sometimes, yes.

But you don't need an AI agent for every process.

If your workflow is: “Whenever someone downloads this ebook, send email A.”

Traditional automation is perfect. You already know exactly what needs to happen.

Agentic workflows become more useful when the correct next step depends on context.

For example: “Research this topic and identify the most promising angle for our audience.”

There isn't necessarily one predefined path. The system needs to analyze information before deciding what happens next. An easy rule of thumb:

If the process is “When X happens, always do Y,” use automation.

If the process is “Here is the goal; determine the best steps to reach it,” agentic AI becomes more interesting.

In reality, the best systems will often combine both.

AI handles decisions where reasoning is useful. Traditional automation handles predictable actions. Humans remain involved where judgment, approval or accountability matters.

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AI agents vs. agentic workflows: what's the difference?

These terms are often used interchangeably, but they describe slightly different things.

An AI agent is the worker.

An agentic workflow is the process the worker, or multiple workers, follows to achieve a larger goal.

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Do you need to build agentic workflows yourself?

Not necessarily. A lot of discussion around agentic AI focuses on frameworks, APIs, orchestration layers and technical infrastructure.

That makes sense if you're building AI systems. It makes less sense if you're a marketer who simply wants to get marketing done.

Look at the repetitive processes you already have.

Where do you constantly:

• Copy information between tools?
• Repeat the same context?
• Rewrite content for different channels?
• Research before every piece of content?
• Coordinate several small tasks to achieve one result?

Those are good places to look for agentic workflows. And that is ultimately the promise behind agentic workflows: less time managing individual AI tasks and more focus on the outcome you were trying to achieve in the first place.