Breaking Down the Walls: How Agentic AI is Reshaping Digital Advertising
The Rise of Autonomous AI Agents and Open Standards is Dismantling Tech Giants' Walled Gardens and Creating a New Era of Transparent, Interoperable Digital MarketingRetryClaude can make mistakes. Plea
For the past 15 years, digital advertising has been dominated by what the industry calls "walled gardens" – closed ecosystems controlled by tech giants like Google, Facebook, and Amazon. These platforms succeeded by creating convenient one-stop shops for advertisers, offering vast audiences and sophisticated targeting capabilities. But their success came at a cost: reduced transparency, limited cross-platform measurement, and advertiser dependence on proprietary systems that prioritized platform profits over marketing effectiveness.
Now, a fundamental shift is underway. The emergence of agentic artificial intelligence – AI systems that can operate autonomously to pursue goals, make decisions, and take actions across time without constant human supervision – promises to transform how digital advertising works. This transformation isn't just about better tools; it's about fundamentally restructuring the power dynamics that have defined the industry for over a decade.
The Walled Garden Problem
Walled gardens like Google, Facebook, and Amazon control their own ads, data, and users, making it hard for advertisers to track performance or run ads across platforms. Google, Meta, and Amazon alone captured 74% of global digital ad spend, creating an oligopoly that has fundamentally altered how marketing works.
The problems with this concentration are manifold. Meta only has access to what users do on Facebook or Instagram, and Google only knows about their activities on Google services, creating fragmented customer views. It's forced advertisers to create separate teams on each buying platform because you have to work within their walled gardens. Perhaps most critically, they have different methodologies for calculating metrics like views, impressions, and interactions, and these don't align across all the walled gardens.
Research from various industry sources reveals the scale of this challenge. In 2020 US digital ad spend was around $165B. The six largest walled gardens accounted for 71% of the spend. Every other publisher fought for the remaining 29%. Even more concerning, in 2017, the "Open Internet" bucket accounted for 47% of total ad spend. Just three years later, it was 29%, a stark decrease in such a short period.
The walled garden approach created what industry experts call a "conflict of interest between buying and selling." The most notable walled gardens being Facebook and Amazon where you can only leverage their data if you also procure media through their own buying platforms. This forced coupling has made it nearly impossible for advertisers to get unbiased performance measurement or optimize across channels effectively.
Enter Agentic AI: A New Paradigm
Agentic AI represents a fundamental departure from the current model. These aren't just tools. They're collaborators, coordinators, and, in some cases, competitors. Unlike traditional automation that follows fixed rules, agentic AI operates autonomously, making decisions with minimal human involvement.
The technology is already showing remarkable capabilities. Amazon Ads has announced a new agentic AI tool that empowers advertisers to easily create professional-quality ads for campaigns, while agencies and ad platforms are using Model Context Protocol and other agent-based buying tools to take over programmatic media.
What makes this different from previous waves of automation? Agentic refers to AI systems which can operate and pursue goals autonomously. These systems, or agents, can develop pathways and make decisions towards an end goal, learning from closed-loop feedback and adapting accordingly along the way, with limited human oversight.
The potential impact is transformative. Tasks like identifying the best audience segments, creating custom audiences, and activation could take anywhere from days to weeks, depending on the number of back-and-forth emails and processes involved. When AI agents are available directly in the hands of agencies and media buyers, they can complete these same tasks in minutes with more accuracy.
The Standards Foundation: Building Open Ecosystems
One of the most significant developments enabling this transformation is the emergence of open standards that allow different AI agents to communicate with each other. Model Context Protocol (MCP) is an open standard that lets AI systems plug into your data sources and tools seamlessly, while Google's Agent-to-Agent (A2A) communication allows an advertiser's agent to communicate with a publisher's agent to secure a custom ad placement at a favorable price – essentially an AI-to-AI negotiation.
These standards are crucial because they prevent the recreation of walled gardens in the AI era. Today's AI agents remain trapped in walled gardens. Agents built on a common platform share similar architectures and common orchestration, data and memory structures, but outside platform boundaries, interoperability standards and frameworks aimed at enabling agents developed on different platforms or by different vendors to work together simply do not exist yet.
The industry is responding with multiple initiatives. OASF (Open Agentic Schema Framework) launched in early 2025, provides standardized schemas for defining AI agent capabilities, interactions, and metadata, while AGNTCY, an industry-standard agent interoperability language backed by Cisco, LangChain, LlamaIndex, and others is working to create universal communication protocols.
By solving critical interoperability challenges like data silos, inconsistent APIs, and discovery complexity, OASF reduces integration costs by an estimated 40-60% compared to custom implementations. This economic incentive is driving rapid adoption across the industry.
Real-World Applications Emerging
The shift isn't theoretical – it's happening now across multiple advertising functions. Google is launching agentic capabilities for marketers to drive greater performance, reduce workloads and build best-in-class campaigns, while Amazon's new creative tool kit can tap into proprietary Amazon shopping data, which, obviously, is not available elsewhere.
The applications span the entire advertising lifecycle. Once the high-level strategy and budget allocation is approved by clients, then the AI agents login to buying platforms, configure campaigns and monitor performance – all with very little human oversight. PubMatic has integrated its assistant into reporting tools to extract complex insights quickly, and is now focusing on integrating agentic AI into the deal management lifecycle.
Even creative production is being transformed. Another example might be multi-agent teams trained on creative workflows, where a strategy agent is focused on extracting consumer insight and shaping campaign direction, another set of agents handles specific idea development, and still another set of agents develops assets for client pitches and ultimately campaign deployment.
The Economic Implications
The economic implications of this shift are profound. Recent industry developments support the thesis about market fragmentation and consolidation pressures, with Microsoft's announcement that it would discontinue Microsoft Invest (formerly Xandr) effective February 28, 2026, removes one of the industry's most transparent DSP options from the market.
The reason? Microsoft cited incompatibility between traditional DSP models and their vision for "conversational, personalized, and agentic" advertising futures. This suggests that entire categories of advertising technology may become obsolete as agentic AI enables more direct connections between advertisers and publishers.
By replacing outdated infrastructure with advanced AI agents, digital ad experts will receive a centralized hub for managing and optimizing programmatic campaigns. This consolidation could dramatically reduce the complexity and cost of digital advertising while improving performance.
Challenges and Considerations
However, the transition isn't without risks. AI agents and "super signal aggregators" could perpetuate programmatic's worst practices, pushing premium media brands into a new era of commoditization. The concern is that AI agents assume control of programmatic buying, further commoditization on the publisher side would also negatively affect advertisers with more sophisticated programmatic strategies.
There are also governance challenges. If AI agents are automating decisions (e.g., shifting budgets, selecting audiences, negotiating deals), agencies must have guardrails. There will be a need for governance policies: setting limits on spend changes an AI can make on its own, ensuring brand safety in any AI-initiated buys, and avoiding biased or ill-advised decisions.
The data readiness challenge is significant. If your data isn't ready, AI won't fix your problems—it will amplify them. Quality and structure vary widely across sources, and rule-based automation doesn't equal autonomous optimization.
Looking Forward: The Open Garden Future
The future of digital advertising appears to be moving toward what could be called "open gardens" – systems that combine the sophisticated targeting and automation of walled gardens with the transparency and interoperability of open ecosystems. Just as standardized protocols fueled the internet's exponential growth, a shared framework is crucial to actualizing a globally interconnected agentic workforce.
This transformation will require significant changes in how the industry operates. Ask your software vendors about their roadmap for AI integration. Are they adopting open standards like MCP and A2A? becomes a critical question for marketing leaders.
The winners will be those who recognize that agentic AI isn't about replacing talent. It's about elevating it. Instead of reacting to data and developing strategies from scratch, they may be setting the guardrails and training the AI agents to act in alignment with the brand and client goals.
Building for the Agentic Future
For marketing leaders, the implications are clear. The era of being trapped in walled gardens is ending, but it's being replaced by something potentially more powerful: autonomous AI agents that can operate across platforms, optimize in real-time, and provide unprecedented transparency into advertising performance.
Organizations that act decisively now to evaluate and implement these interoperability standards will gain significant competitive advantages through faster AI orchestration, reduced integration costs, and faster innovation cycle times. Conversely, enterprises that delay risk being locked into proprietary ecosystems or facing expensive migrations as industry standards solidify.
The question isn't whether agentic AI will transform digital advertising – it's how quickly companies will adapt to harness its potential while maintaining the human judgment and creativity that remains essential for effective marketing. The walls are coming down, and the future belongs to those who can build bridges in the open landscape beyond.