Marketing & Outreach Agents
AI Marketing Automation
A walkthrough of the actual AI agent workflows used to plan, write, and run marketing end to end, from content ideas to live, tracked campaigns.
Client — Tomide Williams (personal practice)
01The brief
The problem
Marketing work is full of small, repeatable tasks, competitor research, ad copy, content drafts, campaign reporting, and each one eats real time if it's done by hand.
Every campaign I ran started the same way: hours of manual research and drafting before a single piece of content or ad was ready to review.
I wanted to show exactly how AI agents built on Claude Code handle that work end to end, from an idea to a published, tracked campaign, using a real marketing calendar as the input.
02Implementation plan
From brief to build
How we turned the client's challenge into a system their team actually uses.
Start from the real marketing calendar
Used an actual content and campaign calendar as the input, not a hypothetical, so every step maps to real deliverables.
Automate the repeatable parts
Built agent workflows for competitor research, ad copy variants, and content drafts, so the first version of everything shows up without a blank page.
Keep strategy and judgment human
Left targeting decisions, budget calls, and final approval with a person, using the agents to prepare options rather than make the call.
Track what actually worked
Connected campaign performance data back into the workflow, so the next round of content and ads starts from what the last round proved.
03Before & After
What changed
Before
- Every campaign started with hours of manual research, drafting, and formatting before anything went live.
- Content and ad copy variants were written one at a time, with no systematic way to test more than a couple of angles.
- Reporting on what worked was a separate, manual step that often happened too late to inform the next campaign.
After
- Research, drafts, and ad copy variants are ready to review within minutes of a campaign brief, not hours.
- Multiple angles get tested each time instead of one, because generating variants no longer costs extra hours.
- Performance data feeds straight back into planning, so each campaign starts smarter than the last.
04Why this approach
The bottleneck in most marketing work was never the strategy. It was the volume of small, repetitive production tasks that had to happen around it before anything could go live.
Automating the research and the first-draft writing freed up time for the parts that actually need a marketer's judgment: what to say, who to say it to, and how much to spend.
It also meant more ideas got tested. Generating a second or third angle used to cost extra hours, so it rarely happened. Now it's the default.
05Impact & benefits
The repetitive production work behind every campaign now runs through AI agents, leaving strategy and approval as the only manual steps.
Campaign prep time
Hours down to minutes
Content variants tested
Multiple per campaign
Manual drafting
Removed from first pass
First-draft research and copy ready within minutes of a brief.
More angles tested per campaign without more hours spent.
Performance data feeds directly into the next round of planning.
Less time on production, more time on strategy and approval.
Consistent output even during weeks with less time to spare.
06Business outcomes
Business outcomes
Automated competitor research and content drafting from a single brief.
Multiple ad copy and content variants generated for real testing.
Campaign performance data looped back into the next planning cycle.
Strategy, targeting, and budget decisions stay with a person.
Works from a real marketing calendar, not a hypothetical brief.
Cuts campaign prep from hours down to minutes.
07Built with
Tools & capabilities
Claude Code
n8nMeta Ads
OpenAI