Agentic workflow examples: eight marketing workflows a small team can run
Agentic workflows without the engineering diagrams or the hype reel. Five patterns and eight marketing examples you can run this month.
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TL;DR
- An agentic workflow is a multi-step process where an AI model does the work in stages, uses tools like search or your CMS, and checks its own output along the way, instead of answering one prompt in one go.
- Workflows follow steps set in advance, and agents decide their own. For most marketing jobs a workflow is the better choice: cheaper, more predictable, and easier to fix.
- Five patterns cover almost everything you'll build: prompt chaining, routing, parallelisation, orchestrator-workers, and evaluator-optimiser.
- The eight examples below are marketing jobs a small team can run this month, from turning a podcast into a week of posts to refreshing old blogs from Search Console data.
- Start with the simplest version that works. Add steps only when the output is visibly better for it.
"Agentic workflows" has become one of those phrases that turns up in every keynote and in very few job descriptions with a clear meaning. Plenty of explanations go full engineering diagram or full hype reel.
This one sits in the middle. A plain definition, the five patterns you'll use, and eight real examples from the marketing side of a small business.
What is an agentic workflow?
An agentic workflow breaks a job into steps and lets an AI model carry them out, using tools and checking results between stages. A normal chat is one prompt and one answer. An agentic workflow might research a topic, write a brief, draft from it, check the draft against your rules, and fix what fails, all without you pasting anything between steps.
There's a useful line between workflows and agents. Workflows are systems where models and tools follow predefined code paths, and agents are systems where the model directs its own process and tool use.
Start with the simplest solution possible and add complexity only when it improves results. That's how good engineering teams build agents, and it's good advice for marketers too. A fixed five-step workflow you understand beats an autonomous agent you can't debug.
The same building blocks show up as four agentic design patterns: reflection, tool use, planning, and multi-agent collaboration. Different labels, same idea. Let the model work in loops and stages, the way a person would.
The five patterns behind most agentic workflows
| Pattern | What it does | Marketing example |
|---|---|---|
| Prompt chaining | Runs fixed steps in order, each using the last step's output | Outline, check the outline, then write the post |
| Routing | Sorts an input and sends it to the right specialist step | Sending inbound questions to sales, support, or content |
| Parallelisation | Runs several steps at once and combines the results | Writing LinkedIn, newsletter, and X versions side by side |
| Orchestrator-workers | A lead model splits the job and hands pieces to others | Researching a market across many competitor sites |
| Evaluator-optimiser | One model writes, another critiques, and they loop | Drafting, scoring against brand rules, and rewriting |
Two classic examples are generating marketing copy and then translating it, and writing an outline, checking it meets criteria, then writing the document. Both are prompt chains, and both are about as unglamorous as AI gets. They also work.
Eight agentic workflow examples for marketing
1. Blog post from keyword to draft
A prompt chain with a quality loop at the end. The workflow pulls the top-ranking pages and the AI Overview for a keyword, notes what they all agree on, finds an angle from your knowledge base, writes a brief, drafts from it, and runs the draft against your voice rules.
We run this exact shape as our ten-stage non-commodity content workflow. At Backbase, a version of it runs in roughly 12 minutes and needs about five minutes of human editing.
2. Podcast or call transcript to a week of content
Parallelisation. One step pulls the strongest ideas and quotes from a transcript, then separate steps write a blog post, three LinkedIn posts, and a newsletter section at the same time, each with its own channel rules. A founder records one honest 45-minute conversation and gets a week of material that sounds like them. Transcripts are the best raw material a small team has, because nobody else owns them.
3. Brand voice checker
Evaluator-optimiser. One model drafts. A second model, given only your voice guide and banned phrases, scores the draft and lists what breaks the rules. The first model rewrites only the flagged sentences. Two rounds usually does it.
4. Content refresh from Search Console
A scheduled chain. Every month the workflow pulls pages that slipped in Search Console, reads each one, checks what now ranks above it, and suggests specific updates: new sections, outdated stats, missing questions. A person approves before anything changes. Automating refresh workflows is usually a content engineer's job.
5. Competitor and market monitoring
Orchestrator-workers. A lead step decides which competitor pages, pricing pages, and changelogs to check this week. Worker steps read each one and note what changed. The lead combines it into a one-page summary for Monday morning. The number of pages changes each week, which is why this needs an orchestrator rather than a fixed list.
6. Inbound question routing
Routing. Messages from your site's chat, contact form, or inbox get sorted by type. Pricing questions go to a draft reply for sales, how-to questions get answered from your docs, and repeated questions get logged as blog ideas. That last branch alone can fill a content calendar.
7. AI visibility check
A scheduled chain with tool use. Each month the workflow asks ChatGPT, Claude, and Perplexity the questions your customers ask, records whether your brand appears and who appears instead, and flags changes. You end up with a list of pages to strengthen, which is LLM tracking on a small budget.
8. Final clean-up before publishing
A short chain that runs last. It checks every link loads, strips stray hidden characters, confirms headings use sentence case, and catches banned phrases. Our free AI slop cleaner runs the character and phrase checks in your browser if you'd like to see what this step catches.
How to build your first agentic workflow
Pick the job you do every week that has the most predictable steps. For most small teams that's turning one piece of content into several. Then write the steps down as if you were briefing a new hire.
Next, write down the context the AI needs every time: your audience, voice rules, banned phrases, and two or three examples of your best work. Without that, every step defaults to the internet's average, and a multi-step workflow just produces generic content faster.
Run the workflow manually a few times, step by step, before you automate any of it. You'll find the step that always needs fixing. Add a check there, or a human review. Then automate.
Keep a person at the end. Autonomous agents need extensive testing and guardrails, and in marketing the guardrail is someone reading the thing before it goes out with your name on it. That person's job shifts towards judgement and taste as AI content team workflows take over the drafting.
When not to use an agentic workflow
If a single well-written prompt gets you 90% of the way, stop there. For many applications a single model call with good context is enough, and agentic systems trade speed and cost for better results.
Avoid fully autonomous agents for anything public-facing until you've tested them hard. An agent that decides its own steps can also decide to publish something odd at 2am. Workflows with fixed steps and a human approval are boring, and boring is what you want near your brand.
Frequently asked questions
What is an example of an agentic workflow?
Turning a podcast transcript into a blog post, LinkedIn posts, and a newsletter section, with each draft checked against your brand voice before you see it. The AI handles several steps and checks without you passing text between them.
What is the difference between an agentic workflow and an AI agent?
A workflow follows predefined steps, while an agent decides its own steps and tool use as it goes. Workflows are more predictable and suit most marketing tasks.
What are the main agentic workflow patterns?
Prompt chaining, routing, parallelisation, orchestrator-workers, and evaluator-optimiser. Most real workflows combine two or three of them.
Do I need to code to build agentic workflows?
Not necessarily. Tools like Claude Code, no-code automation platforms, and content platforms with built-in workflows let you set them up without writing software, though knowing the patterns helps you design them well.
Are agentic workflows expensive to run?
They use more model calls than a single prompt, so they cost more per task. For content work the bill is usually small next to the hours saved, but start simple and add steps only when they pay off.