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UPDATE: This post/guide got much more excitement than expected, we plan on rolling out our Agent externally. Fill this out to learn more!

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Introduction

We started off as a small creative production agency. Back then, our bread and butter was all about making killer ad creatives. We weren’t some big media buying powerhouse. But as clients asked us to handle more and more of their campaigns—across Meta, TikTok, Google Ads, you name it—we got pulled deeper into the tactical weeds of media management.

Suddenly, we were drowning in repetitive tasks: hourly data requests, weekly performance reports, platform-specific “analysis” (read: staring at spreadsheets), and a lot of “I need this ad set changed, like, now” moments. This was not the fun, strategic, big-picture work we signed up for. It was digital grunt work.

We had two choices: hire a bunch of media buyers to scale up this never-ending slog or build something that could shoulder the burden for us. Spoiler alert—we built an AI agent that does exactly that, and it’s one of the best decisions we’ve made.

The Problem—Why We Needed an AI Agent

When we first expanded our services from creative production into media buying, we quickly realized how demanding the operational side of paid media could be. Our small team found itself dealing with:

• Constant Data Requests: Clients and account managers wanted performance metrics, key insights, and cohort reports on a near-constant basis.
• Reporting Overload: Weekly, monthly, and ad-hoc reports started piling up, each tailored to a client’s internal reporting cycles.
• Error-Prone Manual Work: Without automated alerts or checks, small mistakes—like using outdated creatives or miscalculating budgets—could slip by unnoticed.
• Time Sinks: Instead of focusing on strategic initiatives like creative testing or optimizing at a broader strategic level, we were drowning in repetitive tasks that added little value beyond “just keeping the lights on.”

The short version: we needed help. An extra full-time media buyer would run us well into six figures annually—yikes. Meanwhile, building some hacky internal scripts or relying on old-school reporting tools felt like we’d just be rearranging the deck chairs on the Titanic.

Brainstorming Solutions

With the problem clearly defined, we explored several solutions:

1. More Hands on Deck: We considered simply hiring another specialist—a $100k+ annual investment, plus the onboarding and management overhead. Not only was this expensive, but it also didn’t scale nicely with growing workloads.
2. Automated Reporting Tools: Off-the-shelf dashboarding and reporting tools existed, but they were often rigid, lacked the ability to integrate custom workflows, and didn’t proactively identify issues or opportunities. They also required a decent amount of setup and maintenance time.