Marketing Agents Are Too Good Now: How to Build One That Gets Customers

Let’s be real: the hardest part of building a startup today is getting customers. You can vibe code a product faster than ever. Getting people to show up, pay, and stick around is still hard.

That’s why marketing agents matter. But the term has become so watered down that a linear automation with an AI step gets presented as an autonomous marketing department.

Those are not the same thing.

A useful marketing agent has access to business data, makes decisions on a recurring schedule, and improves based on the results. It does not need to run your entire company. Give it one channel, a clear outcome, and the information it needs to do the job.

Facebook ads are a great example. You can build a system that researches customer problems, generates creative, publishes ads, pauses weak performers, and uses the winners to inform the next round. The interesting part is not any individual AI tool. It is how you connect them into a feedback loop.

What Actually Counts as a Marketing Agent?

Cody Schneider’s definition cuts through a lot of the nonsense. A real marketing agent needs three things:

1. Unified business data. It needs enough context to understand the path from marketing activity to business outcomes.
2. Decisions on a cadence. It needs to act repeatedly, not just produce something when you type a prompt.
3. A feedback loop. It needs to read the results of its actions and use them to improve the next decision.

A workflow that generates an image and sends it to a folder can be useful. But it is not the same as a system that publishes an ad, measures whether it produces customers, and changes what it makes next.

We also do not need some mythical general intelligence that invents its own marketing strategy from scratch. I would rather have a focused system that performs a defined process well.

For a Facebook ads agent, that process might be:

• Research the target customer’s pain points and desired outcomes.
• Generate on-brand static images and avatar videos.
• Publish the creative into an ad account.
• Read performance alongside signup, product, CRM, and payment data.
• Pause poor performers and keep strong performers running.
• Generate new creative informed by what is actually producing revenue.

Under the hood, this is code, model calls, business rules, and a recurring decision loop. The infrastructure comes down to a data pipeline, a warehouse, and somewhere to run the agent.

The Startup Sandbox: AI for WordPress

To make this concrete, imagine a product that brings vibe coding to WordPress.

WordPress powers roughly 43% of websites, an enormous installed base reflected in W3Techs’ usage estimates. Yet much of the excitement around AI website building has focused on creating something new, rather than improving the stack millions of businesses already use.

The pitch is simple: a Lovable-style experience built on top of WordPress.

You describe the change you want, and the product helps you change your site. Instead of making customers stitch together forms, CRM functionality, and a collection of other plugins, you bundle the essentials into one package.

A possible pricing model is a base subscription, say $29 per month, that includes a token allowance. Customers use those tokens to build, redesign, and work with the included features. That is an illustrative starting point, not a validated price.

The appeal is bigger than “chat with your website.” It is reducing the cost and complexity of owning a WordPress site.

Start With Plugin Categories That Already Have Demand

I saw how much money moved through this ecosystem when I was a partner in an agency helping major publishers, including Time and TechCrunch, migrate to WordPress. Businesses needed someone to guide the process, and the surrounding plugin economy was huge.

That suggests a repeatable startup approach: find an established plugin category with paying customers, then rethink the job as an AI-first product.

These are product directions, not claims that the existing companies have no AI features:

• SEO: Instead of only flagging problems, an agent could write metadata, restructure content, and add internal links. Yoast is the familiar reference point for the category.
• Forms: Instead of a static form, a conversational experience could answer questions and qualify a lead. Think about the job people use WPForms to accomplish.
• E-commerce: An AI storekeeper for a WooCommerce site could write product descriptions and help create abandoned-cart follow-ups.
• Spam and security: Categories represented by Akismet and Wordfence show how much ongoing operational work site owners already pay to manage.

You can build a focused product in one category, or pursue the bigger bundle: the functionality customers normally buy separately, plus an AI interface for changing the site.

Neither version is automatically a good business. The product has to work. The bundle has to deliver value. And you still need distribution.

Why the Creative Now Does More of the Targeting

For this WordPress product, Facebook ads and Google Ads are natural channels to investigate. The detailed example here is Facebook.

Historically, you might have started by targeting people with a WordPress interest. Cody’s current approach puts much more emphasis on the creative itself.

His practical explanation of Meta’s Andromeda system is that the platform interprets the image, text, video, script, and destination page to help determine relevance. The takeaway is not that audience settings have disappeared. It is that your ad needs to make the customer and the problem unmistakable.

That is why Cody has become so bullish on Facebook for B2B advertising. A very specific problem can be a useful signal, rather than something you need to dilute to reach a broad audience.

For the WordPress product, an ad concept could follow this structure:

• The old situation: paying an agency for routine maintenance and waiting too long for changes.
• The frustration: the site still does not look or work the way the owner wants.
• The alternative: describe changes to an AI tool inside the existing WordPress setup.
• The added value: essential functionality is included rather than sold through a pile of separate plugins.

That angle clearly identifies a WordPress owner with a maintenance and customization problem. The landing page then needs to explain the same promise.

Finally, track meaningful conversion events, including signups and payments deeper in the funnel. Clicks alone do not tell you whether the business is growing.

Step 1: Research the Problems Before Generating Ads

The first job is not making creative. It is finding out what customers actually complain about.

Cody uses Perplexity to research discussions on Reddit because those discussions often contain the language people use when they are frustrated. The research brief is essentially: identify the pain points and desired outcomes of WordPress site owners who might buy a tool that simplifies plugins and lets them change their site through AI.

Then rank the themes by how frequently they appear.

In the WordPress example, three useful themes emerge:

1. Plugin complexity. People are dealing with a stack of tools that must work together.
2. Performance and speed. Owners want functionality without a slow, bloated site.
3. Security and maintenance. They do not want to spend their time worrying about updates, vulnerabilities, or a compromised website.

Customization requiring development skills is another potential angle. So are cumbersome content workflows.

These findings can shape both the advertising and the product. If performance is a major concern, you need a credible answer to it. Cody raises a lightweight delivery approach, potentially using something like Astro, as a direction to explore rather than a finished architecture.

There is also a catch: online discussions contain manipulation and low-quality material. Treat the research as evidence to inspect, not an unquestionable statement of customer demand.

The output of this step is a ranked set of customer problems and outcomes that can seed distinct ad concepts.

Step 2: Build Separate Static and Video Creative Pipelines

Once you know what to say, you can automate more of how it gets produced.

Static Ads: Generate, Then Check the Brand

Cody’s stack uses Kie AI to access image and video models, with Google Nano Banana for static creative.

A useful starting input is a reference ad from a competitor or a related industry. The reference provides a format or visual direction. Your customer research supplies the message, and your brand guidelines supply the constraints.

Those guidelines need to be explicit:

• Which fonts should appear?
• Which colors belong to the brand?
• What visual style should the output follow?
• Is all the text readable?

A vision model can review the generated image against those requirements. That creates a quality-control stage between generation and publication.

The important shift is moving from “make me an ad” to a repeatable production process with inputs and checks.

Video Ads: Turn the Same Insights Into Scripts

For video, Cody is still seeing useful results with HeyGen for AI-avatar, UGC-style creative. The script addresses the pain points or outcomes identified during research.

He is also experimenting with Seedance. The production challenge he highlights is assembling shorter generated clips into a coherent longer ad. Exact model limits change, so the durable lesson is about continuity and stitching, not a particular clip-length number.

The agent coordinates this work through API calls: it conducts research, turns insights into scripts or image prompts, requests assets, and passes the outputs through the next stage.

Generating more assets is only half the job. Knowing which assets create customers is what makes the system useful.

Step 3: Give the Agent a Unified View of the Business

This is the part people skip when they describe an agent that supposedly runs their marketing.

Facebook data alone cannot give you the whole picture. You need to understand what happens after someone clicks.

The relevant sources might include:

• Facebook Ads: spend and advertising performance.
• Google Analytics: website activity and acquisition context.
• PostHog: product behavior.
• HubSpot or another CRM: leads and sales progression.
• Stripe: payments and revenue.

The goal is to connect a specific ad with meaningful downstream outcomes, rather than optimizing for whatever number is easiest to read.

The Pipeline Moves Data; the Warehouse Unifies It

A data pipeline brings information from those separate systems into a shared destination. A data warehouse stores it so the agent can analyze the sources together.

The accessible stack Cody suggests is:

• Airbyte for data ingestion and connectors.
• ClickHouse as the analytical data store.
• A cloud service such as Heroku or Railway for running the agent.

Airbyte and ClickHouse have self-hostable options. AI coding tools can help with setup, but you still need to connect the sources and make sure the resulting data supports the decisions you want to make.

The architecture forms a loop: source systems feed the warehouse, the agent reads the warehouse and changes ads, and subsequent results flow back into the warehouse.

Separate Analytics From Ad-Account Actions

For this architecture, Cody uses the Facebook Marketing API for actions such as publishing ads, pausing them, and making changes that support scaling winners. The agent reads its analytical context from the warehouse.

That separation reduces the temptation to repeatedly request enormous amounts of account data every time the agent makes a decision.

His warning is practical: do not blame “agents” for problems caused by abusive API usage or policy violations. This is an architectural preference, not a claim that legitimate read requests are prohibited. Use the official Marketing API documentation to understand permissions, rate limits, and supported operations.

The Same Data Helps the Human Team

A unified data layer is not only useful for automation. It also enables conversational analytics and custom dashboards.

You can use Claude Code, Codex, or another working environment to investigate business questions across connected sources. If the relevant financial information is included, that might extend beyond advertising into questions about cash collection or accounts receivable.

The principle is the same: useful answers require the right context. A model cannot reason reliably about business information it cannot access.

Step 4: Run the Agent in the Cloud

You do not need a Mac mini sitting in your office acting as a server.

The agent is code. It can run on Heroku, Railway, or another suitable cloud platform. Its recurring job is to read the available data, evaluate it against the objective, and take an allowed action.

That objective should be connected to the business. “Generate more ads” is an activity. “Find creative that produces paying customers” is an outcome.

A sensible progression is to start with a human in the loop. Review what the system produces and recommends. As you understand what is working, give the agent responsibility for more of that defined process.

The point is not maximum autonomy on day one. It is a system you understand well enough to delegate to.

Step 5: Test Creative, Keep Winners, and Feed Back the Results

For one company, Cody describes a production cadence of two new ad sets per day, with five ads in each set. That is ten ads daily.

Those ads are informed by both customer research and historical results stored in the warehouse.

The operating loop looks like this:

1. Publish new tests. Upload the generated creative into Facebook Ads.
2. Allow an initial signal window. In this example, the ads run for two to three days before the first evaluation.
3. Pause the weakest performers. Read performance from the warehouse and act on the results.
4. Keep the winners running. Strong ad sets enter a winners pool and compete for the available budget.
5. Analyze the creative behind the outcomes. Use that information to shape the next batch.

That cadence is an example, not a universal prescription. The broader lesson is to build a repeatable test-and-learn process.

To make the learning concrete, keep a database of what you created. Cody stores the structured image-generation prompts and the scripts sent to video tools. The agent can analyze those inputs alongside performance instead of only knowing that an arbitrary ad ID won.

Now it has a better chance of identifying the message, pain point, or creative approach worth exploring further.

The Failure Mode Nobody Talks About: Creative Entropy

Here is what happens when you only tell an agent to make more of what worked: it can get stuck thinking the same way.

The early results look promising. Then each batch becomes a slightly narrower variation of the last one. Eventually, the system needs fresh input.

Cody calls this the entropy problem. The fix is to keep bringing new material into the creative process.

Use Competitor Ads as Fresh Inputs

The Meta Ad Library gives you a way to inspect ads other companies are running.

Those examples can introduce different hooks, formats, and ways of explaining a problem. They are research inputs, not proof that a competitor’s ad is profitable.

Extract Insights From Industry Conversations

Specialist YouTube channels and podcasts are another source of fresh ideas. WordPress alone has a huge amount of educational content.

Pull out the recurring questions, objections, explanations, and emerging problems. Those insights can become new creative directions rather than another cosmetic variation on an existing winner.

Observe Short-Form Trends

Cody also points to tools such as Viral Loop for researching high-performing short-form content. The useful question is whether recent posts in your category reveal a new format or concept worth testing.

For example, you could examine popular beauty posts from the past week and ask which patterns might inform your own creative.

Marketing is not a campaign you start and then stop. It is a continuous exchange with the market. Your system needs both internal performance data and fresh external observations.

Let the Market Pick the Positioning

The biggest mistake is trying a few ads, seeing disappointing results, and deciding the channel does not work.

Sometimes the underlying product is fine and the positioning is wrong. You may need ten, fifteen, or twenty variations before an angle clicks.

For an AI-first SEO product, one angle might be a competitive alternative to a familiar plugin. Another might be more specific: stop receiving a list of SEO tasks and start getting the work done for you.

Those are different promises, even if they lead to the same product.

Your job is not to impose your favorite message on the market. Your job is to test messages and learn what the market responds to.

Paid advertising is powerful because it shortens that feedback cycle. Cody describes getting useful early direction within roughly 48 hours. That is an initial signal, not a guarantee of profitability or a complete picture of customer value.

Organic content still matters. So does word of mouth. This is not an argument to abandon either. It is an argument against refusing a measurable acquisition channel because you want growth to happen only one way.

Historically, running creative research, production, testing, and optimization at this scale could mean hiring an expensive agency. AI makes more of the production and coordination accessible to a founder.

It does not remove the need for judgment, tuning, or ongoing observation. Anyone promising that you set it up once and walk away is skipping the hard part.

The Same Pattern Can Extend to Other Marketing Jobs

Facebook ads are one concrete application. Other agent opportunities follow the same broad pattern, though each needs its own data, constraints, and operating process:

• Google Ads: agents that help operate and optimize an account.
• Influencer outreach: research creators, initiate outreach, discuss pricing, and flag promising partnerships for a human.
• Cold email: identify potential customers, manage outreach and replies, and help book meetings.
• Short-form distribution: create product-related slideshows, schedule posts, and aggregate performance across accounts where platform rules permit.
• SEO content: research keywords and articles, then write with a defined brand voice and a genuine point of view.
• AI search visibility: research sources cited in a category and pursue relevant brand inclusion through outreach.
• Social media management: coordinate content for channels such as LinkedIn and X.
• Industry media: connect newsletters, researched podcast scripts, synthetic narration tools such as ElevenLabs, and lead magnets into an audience-building process.

These are additional directions, not interchangeable versions of a finished Facebook ads agent. Automating outreach or publishing also means respecting the rules of the channels involved.

The New Marketing Job Is Managing the System

The useful mental model is a focused virtual employee. Give it one channel, the data necessary to understand outcomes, and a defined process for acting.

For marketers, the opportunity is to translate domain knowledge into that system. What would you test? What tells you an ad is weak? What information would change your mind? When does the system need fresh research or human judgment?

AI coding tools make more of the implementation accessible to semi-technical people. They do not replace the expertise needed to answer those questions.

Start with one business, one channel, and one measurable outcome. Connect the data. Build the research and creative workflow. Review the decisions. Then delegate more of the repeatable work.

Vibe coding helps you build the product. A well-designed marketing agent can help you keep learning how to sell it.