How Our Marketing Agency Uses AI Agents to Automate Busywork

The Challenge

Keeping a small, hands-on team ahead of an industry that now moves at AI speed

Mobile app marketing has always moved fast. App Store algorithm updates, ad platform overhauls, new tracking frameworks, fresh creative trends. Staying current was never optional for an agency that takes performance seriously.

What changed in the last 18 months is the speed. AI compressed the cycle. New tools, new ad formats, and new ways of working are landing weekly, not quarterly. Agencies that don’t keep up risk being buried by the ones that do, and “keeping up” no longer means reading a few newsletters on a Friday afternoon.

For a small, senior team like ours, the practical problem looked like this:

• The signal-to-noise ratio in our industry is brutal. There is far more written about app marketing each week than any human can responsibly read, let alone synthesize for clients.
• Manually tracking trends, performance shifts, and ad platform changes was eating into the hours we’d rather be spending on strategy, creative, and client conversations.
• Sharing those learnings across a distributed team through Slack threads, DMs, and tabs that never quite get read was creating its own kind of chaos. Good ideas were getting lost.

We didn’t want to solve this by hiring more people, and we didn’t want to outsource our thinking to a generic AI tool either. What we wanted was to build agents that knew our team, our clients, our workflows, and our standards. Agents that gave us back the one thing every senior marketer needs more of: focused time.

Our Solution

A phased, learn-as-you-go approach to building agents that work the way we do

We approached this the same way we approach client campaigns: as a living system to iterate on, not a one-off project. Across Q1 and Q2 2026, the team committed to weekly AI agent meetings, individual builds, and an internal philosophy that the goal isn’t to automate everything. The goal is to automate the parts of our work that should never have required a human in the first place.

This case study focuses on two of the agents that have made the biggest day-to-day difference: a daily app news agent built by Abbey, and a weekly Meta ads engine built by Rodrigo.

1. The foundation: a team-wide commitment to AI fluency

Before writing a single agent, the team made a deliberate decision to treat AI fluency as a core skill, not a side project. Weekly internal AI meetings became standing time on the calendar, and team members were encouraged to enrol in structured courses, including Anthropic’s Claude and AI Fluency certifications, and bring back what they learned. The agents we built weren’t downloaded off a marketplace and configured to fit. They were built by the same people who manage client accounts every day, which is why they solve the problems our team actually has.

2. Abbey’s agent: a daily scan of the mobile app industry, distilled

The first agent was built by Abbey, our Strategic Director. Every morning, it scans the web for what’s trending, important, and upcoming in the mobile app space: algorithm updates from Apple and Google, shifts in Meta and Google Ads policy, breaking news from major app brands, ASO trends, and new tools worth a closer look. The output is a daily digest the team can read in minutes. What used to require an hour of scattered scrolling is now a single feed by the time the team logs on. It is curated, not exhaustive; it’s a starting point for client conversations; and it runs every day without anyone managing it.

3. Rodrigo’s agents: closing the gap between insight and action

Rodrigo, our Marketing Specialist, has focused on a different problem: the gap between knowing what to do and getting it into the workflow. He built two connected agents we treat as one system. The first is a weekly Meta ads trends and recommendations report that pulls campaign-level signals across our Meta accounts and surfaces the patterns worth acting on, in time for the team’s optimization cycle. The second is an automated ClickUp task creator: when the report or the team identifies a content idea or creative refresh, the agent creates a structured task automatically, categorised by format, assigned to the right person, and pre-populated with context. Insight without execution is just a report. Wiring the output directly into the system the team uses every day ensures nothing falls through the cracks.

4. The operating principle: learn day-by-day, improve continuously

The most important part isn’t the tools, it’s the cadence. Every week, the team reviews what each agent surfaced, what it missed, and what should be added next. An agent that worked well in March is not the same agent running in May. This is the same iterative discipline we apply to client campaigns: set a strong foundation, watch the data, refine relentlessly, never assume the first version is the final one.

Results

More time, sharper insight, and a team that’s getting better at AI every week

The two agents featured here have already shifted how our team operates. The numbers are conservative estimates, directionally honest rather than precision-engineered, because the real result is qualitative: our team gets to spend more of the day on the parts of the job that actually require a human strategist.

• Mornings are now strategy time, not catch-up time. Abbey’s daily news agent reclaimed roughly 30 to 60 minutes per team member, every working day.
• Creative and optimisation cycles are tighter. Rodrigo’s weekly Meta trends report compresses a multi-hour manual analysis into a structured weekly read, with the next batch of creative tasks queued in ClickUp before the team even discusses them.
• Ideas stop disappearing. The Slack-to-ClickUp pipeline means a shared content idea or useful reference becomes a real task with an owner, not a message buried in a thread.

Across the team, that adds up to roughly 50 or more hours saved per week, 40 or more auto-generated ClickUp tasks per month, and a daily news pipeline that runs 7 days a week without anyone managing it. But the number we’re most proud of isn’t on this page. It’s the time our team now gets back to spend with clients.

We are not done. We’re tightening Abbey’s news agent so it learns from what the team engages with, extending Rodrigo’s report engine to more channels and clients, and building agents that don’t yet exist, including one that synthesises Slack and Google Drive into structured client briefs.

“When I started building these agents, I wasn’t trying to automate my job. I was trying to give myself more time to do the parts of it I actually like. The agents handle the scanning, the sorting, the task creation. What’s left is the thinking, the creative, the conversations with the team. We learn something new every week and the agents get a little better every week. That’s the whole point.”

Rodrigo Cortez Solano, Marketing Specialist, Strataigize

Conclusion

What this proves about AI in a marketing agency

AI is not coming for our industry. It’s already here, and it’s already separating the agencies using it well from the ones that aren’t. The agencies that get buried in the next 24 months won’t be the ones who refused to adopt AI. They’ll be the ones who adopted it superficially, bolted a chatbot to their website, and called it a transformation. AI used that way doesn’t make a team better; it makes them faster at producing the same average work.

What we believe, and invest in every week, is that AI is most valuable when it’s built into the workflow by the people doing the work. The goal is never to remove the human. It’s to free up the human’s best hours for the highest-value parts of the job: strategy, judgment, taste, and the conversations only a real person can have with a real client. Three principles guide how we approach this:

• Don’t be afraid of it, and don’t get buried by it. AI is a layer in your workflow, not a replacement for the team behind it.
• Build the agents that fit your team, not someone else’s. The biggest gains come from agents tuned to the workflows, clients, and standards your team already operates by.
• Iterate weekly, not annually. Set a cadence, review what worked, refine what didn’t, keep going. The agent you build in May should not be the one you’re still running in November.