As agentic technologies expand their role in digital advertising, the focus has shifted from what AI can do to how it can actually serve your business. But the reality is that agentic advertising systems are notoriously difficult to scale safely. 

The advertising teams who are successfully navigating the shift to agentic workflows are putting the time, effort, and investment into building the right governance infrastructure that keeps agents operating correctly. This is the necessary foundation required to move these technologies out of experimentation mode and into live production.

This article gives you a high-level, four-step framework that top advertisers are using to safely and effectively scale agentic workflows within their advertising operations. 

How can I make agentic advertising workflows scalable?

“Scalability” is the stress test that your agentic workflows can’t fake their way through. For example, it is one thing for AI to generate ideas or automate isolated tasks. It is another to ask agent-to-agent tools to coordinate campaigns across hundreds or thousands of locations, each with local variables, platform rules, and operational dependencies.

This can only happen if your advertising operations are structured enough to support consistent, reliable, and on-brand outputs. 

The advertising leaders who joined us on stage at POSSIBLE 2026—Andrew Beckman of Location3, Melissa Cartagenca of Storage Rentals of America (SROA), and Anne Thiel of Cox Automotive—have made significant investments in their organization in order to build the right foundation for agentic advertising. 

"I now know that I can launch them within a 24 to 48-hour period of time instead of spending weeks and weeks managing campaigns across multiple networks," said Melissa Cartagenca, Chief Digital Officer and CMO at Storage Rentals of America (SROA).

"Now it's a fluid process,” agreed Andrew Beckman, Founder and CEO at Location3. Breaking down rigid monthly workflows in favor of fluid, real-time execution means his team can “launch these campaigns really quickly at scale.”

So, how did they do it? All three of these leaders shared their experiences, recommendations, and early results from using agentic AI and automation within their advertising operations at POSSIBLE. Here are the four steps to building a resilient agentic infrastructure.  

Step 1: Establishing a system of record is necessary for agentic advertising

LLMs are non-deterministic, which makes them great for understanding intent, translating requests, surfacing options, and helping humans interact with complexity. So why can’t you just plug an MCP into an ad platform and go for it? 

Digital advertising is full of tiny decisions with real financial consequences that can’t be left to variable interpretation. Without a system of record, there is no stable foundation for AI interpretation, orchestration, or execution. 

The right architecture is critical to keeping natural-language LLMs as the interface while simultaneously ensuring that AI execution is deterministic and cost-efficient.  

Use case: Storage Rentals of America builds a unified foundation for agentic advertising workflows

During a presentation at POSSIBLE, Melissa emphasized that starting to build an agentic strategy without a unified advertising data set is like building on a "house with a cracked foundation.” This is why her team is focused on building the right infrastructure first. 

"We centralized feeds from our internal environment and focused on business outcomes, not just digital KPIs,” she said.   

To build the foundation for their agentic approach, Storage Rentals of America (SROA):

• Centralized internal data feeds: SROA integrated internal data directly into their advertising operations to ensure the automation was driven by a single source of truth.
• Integrated real-world business KPIs: Their foundation went beyond standard digital metrics. They incorporated live business data such as occupancy rates, move-ins, and unit pricing.
• Built agentic budget reallocation workflows in Fluency: After establishing their data layer, the SROA team used Fluency to automatically adjust daily budgets based on real-time data. For example, if rates are raised and conversion is impacted, Fluency’s agentic automation workflows automatically reallocate spend to more efficient areas.
• Rapidly scale their approach: Because they took the time to build solid deterministic infrastructure, SROA’s team can now launch advertising for newly acquired locations within 24 to 48 hours: a task that previously took weeks of manual effort.

By building a reliable system of record for their advertising first, Melissa’s team successfully transitioned from manual implementation to a strategic oversight role. This enabled SROA to maintain their current staff while managing a footprint of over 700 locations.

Step 2: Separate AI intent from deterministic execution

LLMs are probabilistic by design: if you ask an LLM the same question five times, you might get five different answers. That’s exactly what you want when you’re riffing on ad copy variations. But it can be very, very, bad if you’re managing hundreds of different accounts, all with specific guidelines and budgets. 

Wiring AI into the wrong parts of your workflow can generate more risk instead of saving you time and effort. For example, if an LLM has direct access to ad platform APIs without the right boundaries, it can delete campaigns or increase bids without your team's knowledge.

Here’s how you fix it: separate AI’s ability to interpret and make recommendations from the system’s ability to make live changes. Your team can use AI where its probabilistic nature is an asset, such as performance analysis, interpretations, or recommendations. Any execution workflows should live in a rules-based system where outputs are predictable and governed.

Fluency's integrated AI, Muse, was built to handle the nuances of advertising operations. Muse surfaces real-time performance trends, KPI shifts, and anomalies across your entire portfolio, giving your team instant insights directly from actual multi-channel campaign data. 

From there, agentic automation tools within Fluency's Digital Advertising Operating System (DAOS) can take over executing on these findings. First, your team sets global governance rules that automatically adapt to the regulatory and strategic requirements of every location in your portfolio. 

When a change needs to happen—say, updating a promotional offer across 1,500 location-based campaigns—your team doesn’t manually update 1,500 campaigns. They only have to update a single "National Offer" tag in the source data for their Blueprint, and agentic automation executes the change across all 1,500 accounts instantly: adhering to pre-set guardrails, with no manual entry and no AI improvisation.

Use case: Location3 eliminates franchise risk exposure using deterministic AdOps execution

Location3 is a high-volume digital marketing agency specializing in complex franchise systems. The sheer volume of daily micro-adjustments across thousands of localized campaigns was creating significant exposure for the agency. Andrew Beckman, CEO and Founder at Location3, highlighted the high-risk nature of manual execution at POSSIBLE 2026.

"There were too many instances of 'fat finger’ errors, like an extra zero or misspelling,” he said during the session. "Managing the budgeting is a headache if you're under-delivering, and if you're over-delivering, it's out of your pocket."

Andrew understood that using unguided AI would be just as risky as manual work: if a human can accidentally add an extra zero to a budget, AI can just as easily misinterpret a prompt and overspend funds.

Instead, Location3 chose to use agentic automation to execute core AdOps functions, like budgeting. Location3 built an architecture that bifurcates AI outputs and agentic execution, giving them safety at scale while protecting the team’s time and the agency’s profitability:

• They use Muse (Fluency’s integrated AI) for high-level strategic tasks like storytelling and data summarization.
• They use agentic automation workflows to handle the movement of money via a rules-based engine.

According to Andrew, combining these two complementary technologies enabled the agency to double their operational capacity while eliminating manual risk.

"Prior to working on Fluency, we had to give our clients a hard cutoff date for creative and budget just so our team could manage the workload,” said Andrew. “Now, it's a fluid process. We launch these campaigns really quickly at scale.”

Step 3: Build governance and guardrails for AI agents

Even with a strong system of record and a deterministic execution layer, agentic advertising workflows need governance. Period.

Governance is what keeps your team’s aspirational strategies aligned with best practices, brand safety, and operational consistency. A strong governance framework should define who reviews the underlying architecture, how strategic decisions are made, and how systems get updated as platforms change.

After all, ad platforms aren’t static. Rules, semantics, approval requirements, and optimization behaviors are constantly evolving on every platform, from programmatic DSPs to TikTok. If you don’t have platform-specific guardrails and operational semantics directing your AI agents, they’ll be no better at carrying out your strategy than a freshly-hired employee. 

Legal compliance is essential, of course. But this stage should also include encoding your agentic systems with a contextual understanding of platform-specific behavior, budget boundaries, brand rules, and your team’s specific decision tree logic. 

It is critical that your human teams set up the controls and rules that guide AI agents to do work that is not just compliant but is contextually accurate. Integrating human-built guardrails is the difference between an agentic tool that knows how to push buttons and one that understands how to act wisely within specific platform or strategic constraints.

Use case: Cox Automotive builds strategic governance for agentic advertising orchestration

During a session at POSSIBLE 2026, Anne Thiel emphasized that moving Cox Automotive into the agentic era required a rigid foundation of oversight. 

"You cannot start building agents without governance," she said. "You also cannot start building without having an idea of what you want that end state to be.”  

Transitioning Cox Automotive’s paid media operations to an autonomous one required Anne and her team to reshape their infrastructure. Anne and the team at Cox Automotive knew that implementing these guardrails was key for the agency to "scale the business and be more consistent.” 

Their ideal state was one where agentic agents carry out the work and humans own the strategy. To build a governed agentic framework, Cox Automotive:

• Established a dedicated, internal AI council: Anne and the leadership team created an operations AI council, with “a set group of people looking at the architecture and making sure run books are updated.” The company saw this as an essential step to scale agentic strategies the right way from the very start.   
• Encoded workflows into agentic run books: According to Anne, the team is "taking existing workflows and translating those into run books: a decision tree to tell the agent, 'if this happens, then do this.'"
• Restructured technical oversight roles: To maintain the integrity of these AI guardrails, Anne moved a project manager into a Knowledge Documentation Lead role to oversee the company’s operational decision trees. She also appointed an Agentic AI Lead to manage the underlying data architecture required to make everything work smoothly. 

Now, with the team in place to maintain the architecture and update decision trees as needed, Anne is confident that Cox Automotive can move seamlessly to the agentic era with total governance.

Step 4: Prepare to upskill AdOps teams from execution roles to agent orchestration roles

Your advertising data is clean, your architecture is set up for deterministic outcomes, and the right strategic guardrails are in place. Now you can begin planning for a different kind of AdOps workforce: one where your human teams stop doing manual AdOps execution and move into strategic oversight roles.  

When your people focus on directing agents to do the repetitive, monotonous work to bring a strategy to life, everything moves faster and more profitably. Shifting your team to these kinds of roles also keeps both safety and scale in balance. 

Agents handle tactical execution while your talented strategies can scale innovation at an unimaginable volume. That means your company’s growth is no longer tethered to the limitations of human execution: your strategic vision is free to scale as quickly as your governed agents can operate.  

Keep in mind, though, that your team shouldn’t suddenly have a new job description once the previous steps are in place. Evolving their responsibilities should be a deliberate, well-paced process. 

Start by being transparent and open about your company’s vision for agentic advertising as you build the foundation. Being intentional about how roles will change and evolve can ensure they feel excited about these changes instead of worrying about layoffs. 

Use case: How Storage Rentals of America redefined AdOps roles for innovation and agentic advertising  

Melissa and her team at Storage Rentals of America (SROA) were constantly running into manual bandwidth limitations. Only two people were managing paid media across more than 650 stores, forcing the company to stick to a single ad channel.

As SROA built their agentic advertising foundation, Melissa began moving her teams into roles where they could spend more time focusing on the why instead of just the how.

“The team that I knew I was going to build was allowed to step back, be more strategic and not worry about the things they were living in day-to-day," said Melissa. 

Melissa understood that this workforce evolution required careful change management. She was transparent about the "ride" the company was on. She acknowledged that, while some team members might not want to transition away from traditional processes, those who remained were able to evolve into the strategic thinkers that this new operational approach demands. 

She also emphasized that, if you’re going to maintain and strengthen your agentic operations, your future hiring efforts should prioritize a specific kind of AdOps professional.

"Make sure the people you bring in are extremely strategic,” she added. “If you have people used to the day-to-day life and they're comfortable, they might not be the right people."

Build a reliable operating architecture for Agentic AI in advertising

Safely scaling agentic AI in advertising is about thinking strategically and systematically: building the right foundation, in the right order, so you can scale your agentic workflows seamlessly and safely.

Start with clean, unified data. Then, establish a system of record using a DAOS, which puts LLMs at the interface while keeping execution deterministic. Work with your team to build strategic AI  guardrails and maintain architectural governance. Lastly, prepare your people to move away from purely operational roles into strategic orchestration roles.

These are the sequential steps that can help your advertising business turn agentic AI and automation into a reliable operating model instead of an aspirational vision or one-off project. Using this framework as an outline, you can use agentic technologies to finally scale your paid media execution without scaling risk at the same rate.

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