Ask the Experts: What Makes Agentic AI Work for Agencies?

Key takeaways

• The hardest part of going agentic for an agency is scale: turning the small workflows and experiments individual team members already built into one repeatable process, without new risk to quality or cost.
• Access used to be the bottleneck for custom AI workflows. It no longer is. The real risk moved from who can build something to whether what gets built matches the standard.
• The fix is structure: deterministic guardrails on the steps that must stay identical every time, paired with a natural-language layer so speed doesn’t disappear.
• This is an operating-model change wearing a technology costume. Agencies that decide what has to stay the same before deciding what to automate get through the transition intact.

Every agency right now is being asked to do two things at once: keep delivering growth for clients today, and rebuild how the work itself gets done for an agentic era. Ask most agencies which half worries them more, and the tension points back to something older than any AI rollout: doing the same excellent work the same way, for every brand, every time. That consistency is what makes an agency valuable, and it’s exactly what’s hardest to hold onto once every team member can spin up their own agent.

We sat down with Gal Zohar, who leads Skai’s data and product strategy, and Courtney Crossley, who leads our Strategic Services practice, to talk through what actually breaks when an agency goes agentic, and what holds.

What’s the hardest part of going agentic, specifically for an agency?

Gal: The hardest part is scaling successful, promising local experiments and proofs of concept (POCs) safely and consistently. Many agencies have great talent creating small-scale workflows, skills, and agents. Turning those into a process that’s repeatable and consistent, yet still customizable, without taking on new risk to quality or cost, is the real challenge.

Courtney: Another real difficulty is staying consistent across dozens of brands at the same time. A single brand team can experiment, learn, adjust. An agency has to make that same decision work identically across every account it runs, or the thing that made it hireable in the first place starts to erode.

Why is consistency harder now than it was before agentic tools showed up?

Courtney: Access used to be the bottleneck. It used to take real effort for one account team to build something custom. Now anyone can stand up a workflow in an afternoon. That’s genuinely useful, and it’s also exactly how twelve versions of “how we do this” show up across twelve accounts.

What happens when every team gets direct access to build with agents?

Courtney: We’ve watched this happen in real time with a partner: a performance lead tried it, loved what it could do, and their very next sentence was about how much chaos it would cause if everyone on the team did whatever they thought was best. That’s not a hypothetical. Giving people superpowers without a shared standard underneath them is how an agency loses the one thing clients are actually paying for.

So is the answer to lock access down?

Courtney: No, and that’s the part people get wrong. Structure is the fix. Keep the steps that need to be identical every time deterministic, then put a natural-language layer in front of them so people keep the speed. What gets built still fits the standard, whoever builds it, and nobody loses access to do it.

Where does the org actually need to change, not just the tooling?

Gal: Roles and workflows, before anything else. We went through this ourselves with our own engineering team: the instinct is to bolt AI onto the process you already have, and it barely moves the needle. The real gain came from asking why the process was shaped that way at all. A lot of it turned out to be built around human handoffs that don’t apply anymore. Once we rebuilt around that, the gains showed up fast.

Is that an engineering problem, or does it apply to agencies too?

Gal: Same problem, different department. Wherever agent capability outpaces the process built around it, someone has to decide what processes, gates, owners, and triggers work best with AI-powered work. . Agencies are living that exact question right now with account teams, the same way we lived it with engineering teams. The way to do this, we found, was to allow the most innovative and knowledgeable folks work in small groups (‘pods’), completely break free from the old processes, start with the mission at hand, and see what works best for them

How do you keep dozens of client teams from all solving this differently?

Courtney: Shared context that sits above any single agent or account, available to every one of them. If every new workflow has to be taught a brand’s history, rules, and preferences from zero, you get different versions of the truth. Put that knowledge in one place, let every workflow draw from it, and consistency becomes the default.

What’s an early warning sign that consistency is already slipping?

Courtney: Two people can’t give you the same number for the same metric, or a workflow’s output depends on who happened to run it. Neither looks like a crisis in the moment. Both are exactly how it starts.

What would you tell an agency leader who’s nervous about losing control once they open this up?

Courtney: The nerves are usually pointed at the wrong risk. What actually matters is whether there are deterministic guardrails around what people can touch. Get that right, and opening access becomes the safe move.

What’s the first thing agencies get wrong?

Courtney: They ask what they want to automate before they ask what they want to stay exactly the same across every account. Answer the second question first, and the first one gets a lot easier.

Gal: And they assume this is a technology rollout. It’s an operating model change wearing a technology costume. Treat it like the second thing, and you skip a lot of expensive lessons.

Both answers point to the same thing. The agencies that get through this transition intact are the ones who work out what has to stay identical across every account before they touch a single workflow. That’s the exact conversation Skai’s Strategic Services and AI Advisory practice has with agencies before any tooling decision gets made. Figuring out the operating model first is what makes whatever gets automated afterward hold up at scale.

Why Agencies Need AI Advisory Support

Sorting out what to automate takes real operating-model work, and most agencies don’t have to do it alone. 

Skai’s Strategic Services and AI Advisory practice works directly with agency leadership on mapping the operating model and setting the guardrails before anything gets automated. 

If your agency is in the middle of this change, that’s the conversation worth having before the next tooling decision gets made. 

Ready to learn more? Talk to Skai.

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