4 Reasons Media Teams Shouldn’t Build AI Agent Systems Alone

1. The Hidden Complexity: Media Workflow Automation Isn’t Easy

Media workflows involve complex systems, fragmented platforms, and campaign logic that’s hard to replicate through code. Trying to stitch it together with your own team can take months—and still fall short.

Modern media operations are intricate, spanning dozens of platforms, channels, tools, and processes – from media planning to ad buying and activation to trafficking, creative rotation, A/B testing, reporting, and analytics. Automating this end-to-end workflow is far more difficult than it looks. What starts as a vision of an AI taking over repetitive tasks soon runs into the reality of legacy systems, data silos, and constantly changing campaign parameters.  

A media team building an agent from scratch must essentially become a systems integrator, stitching together APIs from ad networks, social platforms, analytics dashboards, and internal databases. This “glue work” of connecting 50 or 100 different tools is massive. (For perspective, some specialized AI platforms come with 500+ pre-built integrations to data sources, ad tech and tracked media – the kind of foundational connectivity an internal project would struggle to achieve on its own.) Setting up robust data pipelines and automation rules across all these touchpoints can take months of engineering effort.

Then comes the challenge of rules and exceptions. Media workflows aren’t straightforward assembly lines; they involve conditional logic, creative judgments, and frequent changes. Every campaign is a moving target. An in-house AI needs to handle complex decision trees (if X KPI drops, shift budget to Y platform; if inventory runs out, trigger new creative; and so on). Writing, testing, and maintaining this logic is arduous. It’s easy to automate the low-hanging fruit – say, pausing an ad when spend hits a limit. But handling the full media process, with all its nuances, is another matter. This is why most homegrown solutions fall short of “best-in-class” performance, automating a few simple tasks but leaving a lot of manual work on the table.

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2. The Always-On Maintenance Burden

Agent systems are never one-and-done. They need updates, compliance, error monitoring, bug fixes, and ongoing integrations—all of which steal time from your core marketing work.

Just as marketing campaigns require continual tweaking, an AI that manages those campaigns needs constant updates. New ad platforms emerge, privacy regulations change, algorithms that determine bid prices get updated – your in-house AI must keep up with all of it. That means continuous attention from a talented technical team. You’ll need people to monitor the agent’s decisions, refine its models or rules, fix bugs, incorporate new features, and ensure around-the-clock availability and compliance. Data privacy and security cannot be an afterthought either – yet building robust governance into an AI system is “non-trivial, unglamorous work” that in-house teams have to take on.

For a media department whose expertise is branding and strategy, this level of software maintenance can become an overwhelming sideline responsibility.

In short, the hidden complexity of media workflow automation can turn a simple idea into a resource-devouring beast. CMOs and media leads often don’t realize they are signing up for an internal software product with all the baggage – integrations, data engineering, rule management, maintenance, compliance and more. These are not core strengths of most media teams. As a result, critical resources (your best analysts, your tech-savvy marketers) get pulled away from strategic work and into the weeds of debugging AI software. The opportunity cost is enormous. Every hour your team spends acting as IT support for a homegrown agent is an hour not spent on creative strategy, client insights, or campaign innovation. This is exactly why most companies don’t have the bandwidth to support an AI project of this magnitude in-house: “They don’t have the dedicated resources to develop and support a complicated AI project… and need to focus on their core products.”  

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3. One Agent Isn’t Enough: The Multi-Agent Orchestration Challenge

True media automation requires specialized agents working together, not one jack-of-all-trades. And building an orchestrated multi-agent system isn’t just advanced—it’s closer to setting up your own AI R&D lab.

Let’s assume your media team clears the first hurdle – you’ve hacked together an initial agent that can perform a few isolated tasks (bid optimization here, media data retrieval there). You soon encounter the next trap: scaling that agent into a robust multi-tasking system. Truly automating a media workflow means the AI must handle several different functions in tandem and make them work in concert. For example, one sub-agent might handle budget pacing, another generates media plan variations, another analyzes performance data to adjust targeting, and so on. In AI research, this is the realm of Multi-Agent Systems – where a collection of specialized agents or models collaborate to solve complex problems. It’s powerful, but it’s a level of sophistication far beyond a single chatbot or script.

Homegrown efforts often start with a single AI model – say a large language model (LLM) – that they try to stretch across all tasks. This “one-size-fits-all” approach quickly reveals its limits. A single model might do okay at some tasks and terribly at others. Perhaps it writes decent media strategies but fails to analyze spreadsheets, or it can adjust bids but can’t understand why a creative isn’t resonating. Moreover, using one model end-to-end creates a fragile pipeline: if it makes an error at one step, the whole process derails (these are the “cascading failures” and unpredictable outputs that AI engineers know too well.

In technical terms, relying on one agent can result in lower accuracy and reliability, as errors compound without checks. For media applications, that could mean big mistakes – overspending a budget, misinterpreting a trend, or generating off-brand content – slipping through unchecked.

The proper solution is an orchestrated multi-agent system: different AI agents (or models) each focused on what they do best, coordinated by an overarching logic. For instance, a “Project Manager” agent could break the media workflow into sub-tasks and assign each to a specialist agent (one tuned for media planning another for data analysis, another for budget math, etc.). Checks and balances are inserted between stages – e.g., a “Supervisor” agent verifies outputs before the next step. This design dramatically improves reliability and results. In fact, computer scientists report that such agent orchestration improves accuracy by mitigating individual model errors and balancing the load across specialized agents.  

However, building a multi-agent orchestration in-house is a herculean challenge. It’s no longer a weekend project tweaking an API; it’s architecting an entire AI system of systems. As Forrester analysts noted, “these architectures are convoluted, requiring multiple models, advanced RAG (retrieval augmented generation) stacks, advanced data architectures, and specialized expertise.”  

In plain language: you need a team that knows how to juggle various AI models, possibly combine large language models with knowledge bases (that’s the RAG part), manage a distributed workflow, and ensure it all scales and stays stable. Most marketing orgs simply don’t have this depth of AI engineering talent in-house. It’s one thing to fine-tune a ChatGPT on your data; it’s another to engineer a complex choreography of agents working 24/7 on live business tasks tailored to advertising needs and vertical knowledge.

In other words, you can either invest heavily in building an internal mini-AI research lab, or you accept that this kind of orchestration is best left to external platforms built for the task. For most media organizations, the latter makes far more sense.

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4. The Blank-Slate Burden: Teaching an AI Agent from Scratch

Even if you build the system, your first AI agent still starts out like clueless intern. Training it with your brand logic, KPIs, and best practices is a long, error-prone, and costly road.

Imagine hiring a new junior employee with no media and advertising experience and trying to train them to be as effective as a seasoned media planner. You’d invest months in onboarding, shadowing, and mistakes before they start adding real value. It’s similar with an AI agent. Unless it’s pre-trained on media-specific knowledge, it will make a ton of rookie mistakes early on. It might not understand your performance KPIs, your optimization tactics, or the compliance rules you must follow. So, your team enters a tedious phase of “educating” the AI: feeding it historical campaign data, writing detailed prompts and rules, correcting its errors, and iterating. This is the expensive “teach the agent” phase that unfortunately comes after you’ve already sunk costs into building the system. It’s like building a fancy robot, then realizing you also have to homeschool it before it can do the job.

The costs of this training phase are both direct and indirect. Direct costs include labeling data or writing countless examples for the AI, fine-tuning machine learning models on your datasets, and the compute resources to run those experiments. For instance, assembling and preparing all your past campaign data to train an AI model can be a massive undertaking (one report noted that “sorting through internal information and translating it into usable data” is a major time sink for in-house AI projects).  

The indirect costs are the opportunity costs and the risk of poor early performance. During the learning curve, your team still has to double-check the AI’s work or handle tasks it can’t, which means you’re not freeing up as much time as hoped. Worse, if the AI makes mistakes with real campaigns, it could waste media budget or cause an issue with a misguided ad – risks that are hard to tolerate in a live business environment. All of this erodes the ROI case for building in-house. It’s no wonder that in-house AI initiatives often take far longer than planned to achieve value, if they ever do.  

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Road to Part II: Pre-Trained Agents - Ready on Day One, Working with Your Media Workflow & Data

DIY agent systems sound empowering—but they quickly trap media teams into roles they never signed up for: AI product owners, compliance auditors, dev team managers. The result? Slower time-to-market, mounting frustration, and limited ROI.

In Part II, we’ll show you the smarter alternative: pre-trained, workflow-integrated AI agents that launch fast, evolve with your strategy, and deliver results without turning your team into a dev shop. If Part I was the reality check, Part II is the roadmap forward: read it now at this link.

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