Generative AI and agentic systems promise transformative business value, but most organizations struggle to move beyond initial prototypes to scalable implementations that drive meaningful impact. 

The core issue is rarely AI itself; instead, it’s the underlying data. AI is only as good as the data foundation it runs on. Without trusted and unified data, even the most advanced AI agents will deliver incomplete, inconsistent, or misleading results. 

Data products for an agentic world 

To address this challenge, forward-looking organizations are adopting and deploying data products to obtain more value from their data. Effective data products are structured, governed, and reusable data assets packaged for consistent consumption across your organization. By treating the data as a product, you can curate data for reuse and store it within a centralized catalog, creating an internal marketplace where teams can access trustworthy information easily and efficiently.  

For AI agents, this means that data products are: 

• Discoverable and self-contained: Easy to find, documented, and versioned. 

• Domain-oriented: Curated for business-specific tasks. 

• Interoperable: Accessible via APIs, files, or events for RAG and real-time use. 

• Governed and secure: Compliance and dynamic access controls baked in. 

• Managed like a product: Defined SLAs, ownership, and continuous improvement. 

Think of data products as the fuel that powers AI, whether for classic ML models or GenAI copilots. 

5 steps to building trusted data products for AI agents 

1. Start with foundational data products  
   Align your data products with source systems and group by domain (e.g., customers, transactions, etc.). Most organizations deliver foundational data products to data consumers and AI Agents – but that’s where it stops. If these data products are based on siloed information, they are likely to have duplicates, making it difficult for consumers—whether human or AI agents—to determine and extract what’s valuable for decision-making. 
   

2. Evolve to enterprise-wide data products  
   By building on a unified and enriched data foundation, you can bring together data from multiple sources, deduplicate it, and then enrich it with third-party data. For example, with customer data, this means you can add households, transactions, and corporate hierarchies on top of existing internal data, which enables you to bring more context into your data products. Context results in a higher trust in the data and, ultimately, faster, smarter, and more transparent decisions. 
   

3. Plan for multiple delivery methods 
   Design data products for multiple delivery mechanisms such as batch, events, and APIs to be available in multiple ways depending on the consumer. For example, AI agents using RAG need on-demand APIs, and real-time applications often need events (to indicate data changes) and APIs. Whereas data consumers may need static files.  
   

4. Embed governance early  
   Define ownership, set up access policies, and agree compliance controls upfront. Data products, served via multiple delivery methods, need to be consistent to be trusted.  
   

5. Turn data products into graphs available for AI Agents  
   Design dynamic graphs based on enterprise data products, including relationships across data, that are relevant to what the AI agent needs. As a pioneer and leader in graph generation, we call this a contextual fabric, the missing ingredient that makes our platform perfect for AI agents to find and understand. 

Delivering AI-ready data products with Quantexa 

Quantexa enables organizations to build and deliver trusted, contextual enterprise data products through: 

• Rapid data ingestion: Quick and easy data preparation even with varied schemas, bringing together billions of internal and external data.  

• Category-leading Entity Resolution & Graph Generation: Data matching with unparalleled accuracy, and rich contextual graphs that LLMs thrive on. 

• Contextual RAG for AI Agents: Quantexa’s Agent Gateway provides on-demand, graph-based data access, enabling AI agents to answer complex queries with accuracy and context. 

With Quantexa, organizations can bridge the gap between data and AI, ensuring that every agent interaction is grounded in trusted, contextual data, driving genuine business outcomes like improved risk management, fraud prevention, and revenue growth. 

The bottom line is: AI agents can’t fix bad data. To unlock their potential, build a strong data foundation through governed, reusable enterprise data products.

Take our assessment and find out how Quantexa can help you build a solid data foundation to scale AI programs.