AI Marketing Agents for Campaigns and SEO
Use Mrrlin to run AI marketing agents for campaign planning, SEO briefs, content workflows, landing pages, reporting, and approval gates.
Chat tools vs Mrrlin
The execution layer beats another blank chat window.
Chat tools make you re-send context and manage the work. Mrrlin keeps project memory and spends tokens deliberately.
Why it matters
More copy is easy. Copy that sounds like you is the hard part.
Mrrlin helps AI marketing agents operate from your positioning, offers, site context, approved claims, and review rules, then returns drafts and deliverables with evidence attached.
Workflow
From campaign goal to an approved campaign pack.
Load the brand before drafting
AI marketing agents start from the wiki: positioning, offers, approved claims, exclusions and past reviewer notes. If the goal, say a webinar campaign for operations leaders, needs something the wiki lacks, like the registration deadline, the question lands in your inbox first.
Plan angles, then commit
The Director turns the goal into tasks: audience notes, message angles, a landing-page draft, social and email drafts, and QA. When two angles are plausible and the evidence is split, you pick one; the Director does not quietly guess for you.
Draft, critique, redraft
One model drafts while reviewers on other models check tone, claims against evidence and fit with the offer. Page titles can be compared across three models before one is chosen, and weak lines get another draft before you ever read them.
Sign off the package
You approve the campaign as a set: page copy, post drafts, email drafts and the checklist, with review notes showing which claims were rewritten. Publishing and sends wait on that approval, and the accepted wording is saved as brand memory for the next campaign.
Evaluation checklist
What to check before you commit.
Brand memory matters
Marketing agents should remember positioning, approved claims, exclusions, customer language, and previous reviewer notes.
Separate drafts from decisions
Good workflows let agents draft and critique quickly while campaign angles, publish steps, and sensitive claims stay approval-gated.
Measure the finished package
Judge the tool by whether it returns a usable campaign pack, not by how many disconnected copy variants it can generate.
Use cases
Where it fits.
Campaign planning
A quarterly goal like “fill the partner webinar” becomes a campaign plan with audience notes, three message angles, a channel mix and dates. Each angle cites the positioning or customer evidence it rests on, so choosing between them is a decision rather than a guess.
SEO content briefs
For organic content, agents draft briefs that set the reader's question, the angle, the pages to link to and the claims the article may and may not make. Title options are compared across models, and the brief is approved before anyone writes a paragraph.
Landing-page production
Claude Code builds landing variants on a separate branch in its own git worktree, never your checkout, with copy pulled from the approved brief. Mobile and claim checks run before the page is proposed, and it reaches the live site only through a deploy you approve.
Social and email drafts
LinkedIn post drafts and email sequences come from the same brief as the page, so the promise stays consistent across channels. A tone reviewer flags lines that drift off-brand, and nothing is posted or sent until you approve it.
Comparison
Content generators versus agents that remember your brand.
Keep reading
More on AI workflow automation.
Start from the AI workflow automation overview, or go deeper with the guides next to this one.
FAQ
Before you start.
Can AI marketing agents publish or post without us?
Not by default. Publishing, emailing real contacts and client-facing claims are approval points from the start, and outside destinations such as your site or an ad account are reached only through a grant you create, with each call logged. Agents can draft, critique and redraft freely; the public step stays with you.
How do AI marketing agents learn what we will and will not say?
Through the wiki and the review loop. Approved claims, exclusions and customer language are stored as project memory, and when you reject a line or accept a rewrite, that decision and its reason are written back. The next campaign starts from those notes instead of a blank prompt.
Will this work for an agency running several client brands?
Each client can live in its own project with its own positioning, approved claims and reviewer notes, so voices do not bleed across brands. Ad accounts are handled per client through grants, and deliverables are packaged for client review, with sensitive claims held for your sign-off before anything reaches the client.
Tell us the outcome you want AI to execute.
Share the workflow you want to automate. We’ll map the first Mrrlin run — plan, agent routing, review loops, and approval checkpoints.
No credit card · No migration · One goal