Smarter AI Agents Thanks to Data Fabrics

What happened?

Data fabrics, platforms that unify access to data scattered across warehouses, lakes, SaaS, and data centers, are being rediscovered as critical enablers for smarter AI agents. According to InfoWorld, these systems provide a semantic layer that allows AI agents — such as those from Salesforce or ServiceNow — to query heterogeneous data sources in real time while respecting unified governance policies. Although the data fabric concept is not new (Gartner included it in its hype cycle since 2019), its convergence with generative AI and autonomous agents has skyrocketed its relevance. Companies like SnapLogic, Talend, Informatica, and Denodo already offer advanced capabilities in this direction, and the data fabric market is expected to grow from $2.5 billion in 2025 to over $5.7 billion in 2028, according to IDC.

Why is it important?

Current AI agents often operate on limited, predefined datasets, restricting their response capability and context. A data fabric expands the scope of accessible data, integrating structured and unstructured sources, and applying consistent security policies. Dominic Wellington, data and AI expert at SnapLogic, describes the data fabric as "the connective tissue that ensures accessibility, availability, and uniform understanding of data across the organization." This contrasts with previous approaches like centralized data warehouses, which required moving and transforming data before use, introducing latency and costs. Data fabrics enable virtual queries on data in place, reducing replication and improving data freshness. Additionally, they integrate with the Model Context Protocol (MCP), an emerging standard that allows AI agents to connect with external services like CRM or ERP APIs, further expanding their reach.

Consequences and outlook

The adoption of data fabrics for AI will bring multiple impacts:

• More contextual agents: They will be able to respond with up-to-date information from the entire enterprise, not just a silo. For example, a customer service agent could simultaneously query purchase history (data lake), inventory status (ERP), and return policies (SaaS) in real time.
• Unified governance: Access and privacy policies are applied centrally, reducing data leakage risks. This is critical in regulated industries like healthcare or finance, where traceability and compliance are mandatory.
• Democratization of AI: Small and medium-sized enterprises can also benefit, as data fabrics are available as cloud services (e.g., AWS Lake Formation, Azure Purview). This lowers the entry barrier, though integration complexity remains a challenge.
• Integration challenges: Implementation requires connecting multiple legacy systems and maintaining data quality. According to Gartner, 60% of data fabric projects fail in their first year due to governance issues and lack of skills. Additionally, latency in queries over distributed sources can be a bottleneck if caching or virtualization is not optimized.

"Data fabrics are not just for large enterprises; today even smaller companies need them as part of their AI democratization programs," notes the InfoWorld article. This democratization, however, must be accompanied by data literacy to avoid misinterpretations.

A relevant historical case is the rise of data lakes in the mid-2010s, which promised cheap, flexible storage but devolved into "data swamps" due to lack of governance. Data fabrics learn from that mistake by imposing quality and lineage policies from design.

What readers should know

When evaluating a data fabric for AI agents, consider:

• Support for structured and unstructured data: Some platforms focus on analytics and ML, with limited support for unstructured data like PDFs or images. Verify if your use case requires such data.
• Real-time query capability and data lineage: Data virtualization allows queries without moving data, but can introduce latency. Lineage is crucial for audits and debugging.
• Integration with the Model Context Protocol (MCP): MCP standardizes how AI agents connect to external sources. A data fabric that supports MCP will ease integration with tools like LangChain or AutoGPT.
• Centralized and automated governance policies: Look for data masking, role-based access control, and compliance with regulations like GDPR or CCPA. Automation reduces manual burden and errors.

Platforms like SnapLogic, Talend, Informatica, and Denodo already offer advanced capabilities in this direction. The trend is clear: data fabrics will become the backbone of the next generation of enterprise AI agents. However, organizations must carefully plan implementation, prioritizing data quality and training multidisciplinary teams. As Wellington noted, "a data fabric is not a silver bullet; it requires a cultural shift toward data governance as a shared responsibility."