Blog / Insights
.jpg)
“AI’s outputs are only as good as its data inputs.” In the rapidly emerging agentic AI era, this phrase may be cliché, but it’s never been more relevant.
If AI agents are the king of this new era, then data is the queen. And without its queen, the king doesn’t stand a chance. Agents do far more than just generating content. They make decisions, take actions, and operate at scale. But that power is only as strong as the underlying data guiding it, meaning that high-quality, reliable data is what enables AI to move from insights to action.
This is also true in the advertising ecosystem. There’s a common trap: “90% of AI demos are all icing, no cake.”1 It’s easy to build something that looks impressive on the surface. It’s much harder to build something that actually works. Something grounded in reliable, structured, and real-time data that can support scalability, security, and true business integration. For example, agents must deliver consistent, reliable outputs fast. Because they can operate autonomously, strong observability is essential for monitoring decisions, debugging interactions, and ensuring processes remain deterministic. None of this is possible without a robust data infrastructure that supports and guides every action the agent takes.
Think of agents like talented chefs in the same kitchen, using the same tools and appliances. The only difference is that one has access to a richer pantry, more variety, fresher ingredients, and higher-quality inputs. That chef will consintently deliver a better meal. Data is like that pantry that defines the quality of the final output.
Ultimately, data acts as the connective tissue for an agent’s intelligence. To be effective, an agent requires a unified layer of high-fidelity context to make superior decisions. By providing accurate, structured, and rich data at scale, organizations enable their agents to move beyond simple retrieval and toward true, reasoned decision-making.
As a result, in the agentic era, choosing the right partners becomes critical. Advertisers must rely on partners that provide high-quality, well-governed data capable of meeting these standards to power effective AI.
What data characteristics matter when selecting an agentic AI partner
Scalability and accuracy
Agentic systems operate at speed and scale, so the data behind them must be both highly accurate and scalable across large, complex environments. Inaccurate or inconsistent data doesn’t just slow performance; it leads to flawed decisions. High-quality, real-time data ensures that agents can continuously optimize and deliver consistent results.
Integrity, security and governance
No matter how advanced an AI agent is, its performance ultimately depends on the quality and integrity of the data feeding it. In the advertising world, data for any given use case is often assembled from multiple partners, e.g., conversions, impressions, audience signals, and maintaining integrity across all these sources is essential to preserve the quality of the final output. If poor-quality data enters the system, it can quickly compromise everything else. It’s essential that this data be privacy-centric, respect consumer choice, and adhere to relevant regulations. It’s also important that governance frameworks are in place to detect errors, eliminate inconsistencies, and prevent outdated information from shaping decisions. Strong governance ensures data is handled responsibly and that AI outputs meet ethical and regulatory standards, reducing both legal and reputational risk.
Integration into real business workflows
Agents should not operate as standalone tools, they must be seamlessly embedded within real business workflows to deliver meaningful value. To be truly effective, these agents need to be reliable and flexible, enabling users to integrate them into their day-to-day processes and decision-making. They should support a wide range of use cases without requiring specialized expertise, ensuring adoption across different teams and functions.
Users should be able to incorporate their own data sources, apply custom logic or rules and adapt outputs to align with their specific goals and contexts. In this model, the agent provides a strong foundation for intelligence while allowing users to build on it. The result is a system that combines core capabilities with user-driven customization, making AI not just powerful, but practical, adaptable, and aligned with real-world needs.
The Yahoo DSP data advantage in the agentic AI era
At Yahoo DSP, we place data at the heart of our agentic AI, just as we do across our entire platform. Layered on top is our strong identity framework that stitches this data together in a meaningful way, ensuring scale, quality, and accuracy. Additionally, our AI models continuously learn from every campaign executed, impression delivered, and conversion attributed, keeping our agents up to date, adaptive, and constantly refined by campaign performance.
Additionally, Yahoo DSP takes data privacy and security extremely seriously, and that responsibility extends to our Agentic AI capabilities. Our approach is built on open standards, enabling interoperability through MCP servers and APIs without the need for complex custom integrations. To ensure accountability, we maintain strict human oversight and approval mechanisms for any action-taking workflows. Only partner-approved authorized agents can integrate with the Yahoo DSP data, tools and capabilities, and users are always in control, with clear prompts to review and confirm decisions before execution. At the same time, our architecture enforces strong data separation and security by design, ensuring that agents access only scoped, permissioned data through secure API interactions.
Finally, the Agentic AI can be integrated into the advertisers’ desired business workflows to deliver real value. With a flexible, interoperable approach, clients can decide when and how to use Agentic AI, putting the choice and control in the advertisers’ hands. We enable them not only to use our built-in agents embedded in our platform but also to bring in their own agentic logic that enables them to create customized solutions tailored to their specific use cases and needs.
To learn more about our agentic AI approach and how you can leverage it, reach out to your Yahoo DSP representative.
1 Charafeddine Mouzouni, LinkedIn
––
About Srinivas Bhagavatula
Srinivas is a Sr. Principal Architect with over 20 years of experience in Big Data, data privacy, and real-time user targeting pipelines, and has spent the last two years building GenAI-based solutions. He takes a user-first approach to designing systems that balance speed, accessibility, and privacy at scale. Outside of work, he’s a dedicated Ravens fan—he bleeds purple—and now enjoys watching games with his young kids.
––
About Dhaval Khamar
Dhaval leads Product Management for AdTech Data Platforms, Audiences, and 1P Measurement at Yahoo DSP. He focuses on building scalable, data-driven ecosystems that help advertisers discover audiences, activate campaigns, and measure performance effectively. Dhaval is passionate about simplifying complex technology and making data and AI accessible for marketers. He also believes that somewhere in the universe, there’s a perfectly optimized system—but like most data platforms on Earth, we’re still a few iterations away.
