Develop smarter AI agents with data fabrics

Data fabrics are emerging as a critical infrastructure layer for developing smarter AI agents, enabling centralized data access and governance across scattered enterprise data sources. Experts from SnapLogic, SAP, EvoluteIQ, Typeface, and Appian argue that without a unifying data fabric, multi-agent AI systems risk working at cross-purposes and cannot deliver trustworthy, context-aware decisions.

Every organization has data scattered across data warehouses, data lakes, SaaS platforms, cloud drives, and data centers. Data fabrics enable organizations to centralize and control data access, making it easier for users, such as data scientists and citizen data analysts https://drive.starcio.com/2026/03/citizen-analytics-ai-era-cios/ , to find and use trusted and governed data sources. Data fabrics, data meshes, and distributed data clouds https://www.infoworld.com/article/2338426/how-to-explain-data-meshes-fabrics-and-clouds.html are all platforms to help IT and data teams put some order to the chaos around the myriad of data sources they support. Large companies need data fabrics https://www.infoworld.com/article/3497094/does-your-organization-need-a-data-fabric.html due to the volume and variety of their data sources. “A data fabric can be thought of as the connective tissue that ensures consistent accessibility, availability, and understanding of data across an organization,” says Dominic Wellington, data and AI expert at SnapLogic https://www.snaplogic.com/ . “Individual siloed platforms may have their own internal data transfer systems, and particular teams or departments may adopt interchanges that work for that domain, but a data fabric operates at a higher level, ensuring that unified data policies are applied end-to-end across the entire enterprise.” When reviewing data fabrics, it’s important to consider their primary use cases, supported data types, data processing capabilities, data management structures, and governance functions. Below are some considerations when reviewing data fabrics as features, platforms, and stand-alone products. Data fabrics are not just for enterprises, and today, even smaller companies need them as part of their AI democratization programs https://www.cio.com/article/4136302/how-to-get-ai-democratization-right.html . Here are a few reasons why: “As AI agents move from generating insights to taking action, the data fabric becomes foundational in the agentic era,” says Irfan Kahn, president and chief product officer of SAP Data & Analytics https://www.sap.com/index.html . “Most enterprises operate across scattered data sources and diverse data landscapes, and what’s needed is shared business context, governed access, and clear accountability for how data is used in decision-making. Without that context, agents can’t fully understand or coordinate across the enterprise to deliver meaningful value.” Sanjay Koppikar, chief product officer and cofounder of EvoluteIQ https://evoluteiq.com/ , adds, “Multi-agent architectures become untrustworthy when a unifying data fabric architecture is missing, since agents will often work against each other in the service of their own objectives.” AI agents need a combination of real-time data, user information, problem details, and historical context to guide their decision-making. Vishal Sood, president of research and development at Typeface https://www.typeface.ai , says, “MCP and data fabrics give agents access, but the harder problem is contextualizing data across multiple sources and ensuring the underlying content, media, and unstructured data are trustworthy.” Data fabrics are the foundational elements for providing current information and long-term memory to AI agents. They simplify the many-to-many problem of connecting multiple AI models, AI agents, and MCP server integrations to multiple structured and unstructured data sources. “The data fabric does a beautiful job of encompassing three concepts needed to create applications and processes: the data catalog, the data model, and data access,” says Sanat Joshi, executive vice president of product and innovations at Appian https://www.appian.com . “But now add business rules, process models, APIs, security groups, the organizational model, and their interrelationships into one unified view of the enterprise, and that becomes your context layer.” Devops teams just getting started on an AI agent proof of concept may want to connect directly to the optimal data sources and APIs. Michel Tricot, CEO and cofounder at Airbyte https://airbyte.com/ , says connecting agents to live APIs is a great start, but it creates two big problems: APIs only return data that an agent already knows to ask for, and every query is an expensive API call chain that, with overhead, can overwhelm infrastructure in production volumes. Tricot says the data fabric for AI use cases must be dynamic, leveraging discovery of available information from replicated data, fetching live contextual information, and writing the data back to business applications to update records. Moving data in and out of the data fabric requires an integration strategy. Zero-ETL https://www.datacamp.com/blog/what-is-zero-etl extract, transform, load is one low-cost, efficient approach for connecting to structured data sourced without replicating information. Once information is accessed centrally, it also enables streamlined security and governance. “The promise of AI agents breaks down when they’re stuck waiting on brittle ETL, dealing with poor data quality, and lacking the right context to perform analysis,” says Preston Wood, chief security and strategy officer at Databahn https://databahn.ai . “Generating AI-ready data within a data fabric gives agents real-time access to operational data without the latency and drift that undermine decision quality. A well-architected data fabric provides the governance and lineage controls that let you deploy agents confidently, knowing exactly what data they’re touching and why.” Data fabrics centralize AI-ready data https://www.infoworld.com/article/4091422/how-to-ensure-your-enterprise-data-is-ai-ready.html and help data governance teams address data quality https://www.infoworld.com/article/3667314/3-data-quality-metrics-dataops-should-prioritize.html issues, biased data https://www.nature.com/articles/s41597-022-01705-8 concerns, privacy compliance https://drive.starcio.com/2026/02/data-privacy-week-leadership-accountability/ , and other data governance non-negotiables https://drive.starcio.com/2024/10/6-important-ai-and-data-governance-non-negotiables/ . Data fabrics also help address integration issues, monitor for data pipeline errors https://www.infoworld.com/article/3487711/the-definitive-guide-to-data-pipelines.html , and report on performance latencies. The result is that AI agents, models, and other analytics capabilities can then connect to trusted data sources with consistency. “As AI agents and MCP architectures increasingly rely on data fabrics as their golden source of truth, data quality stops being a hygiene problem and becomes a trust problem, as we all know that trust is foundational to autonomous decision-making,” says Kellyn Gorman, database and AI advocate and engineer at Redgate Software https://www.red-gate.com/ . “Organizations that invest now in semantic consistency, lineage tracking, and observable data contracts across data fabrics will be the ones whose AI agents can be trusted to act without constant human correction.” Data fabrics that support zero-ETL and other bidirectional integrations with sources thus become an organizational knowledge base, the data source for training AI models, and a foundation for producing data metrics. “AI agents are only as reliable as the data they’re built on, and most organizations underestimate how much implicit tribal knowledge lives in their transformation logic rather than their source systems,” says Tobias Ostwald, director of analytics at NMI https://www.nmi.com/ . “If you’re exposing a data fabric to agents or MCP integrations, you need lineage, testing, and metric definitions baked into the layer itself, not just documented somewhere, because the agent can’t call a colleague to gut-check a number.” With a data fabric in place, governance, security, and other risk management leaders have a central location to manage data security, centralize access controls, and fulfill other governance responsibilities. Miles Ward, CTO of AI in Solution Lines at Insight https://www.insight.com , says, “We have to move past security by isolation to a governance model where the fabric itself enforces the pavement and walls of compliance.” The data fabric also governs entitlements for AI agents and their users. Centralizing these business rules can help organizations avoid creating AI debt, a risk if controls are implemented directly in data sources or consumers. “The convergence of AI-generated code sprawl and autonomous MCP connectivity creates a ‘perfect storm’ of architectural drift and toxic permission combinations,” says Karen Cohen, vice president of product at Apiiro https://apiiro.com/ . “Effective governance requires a security data fabric that monitors these autonomous connections in real time to enforce intent-based policies and strictly limit agent scope to its specific purpose. By integrating guardrails that align AI-assisted development with secure architecture principles, enterprises can proactively secure their expanding attack surface without sacrificing developer velocity.” Expect vendors to expand the scope of their data fabrics beyond text and documents. Some will include specialized document processing https://www.infoworld.com/article/3833936/improving-intelligent-document-processing-with-generative-ai.html for common formats such as invoices, contracts, and product documentation. There will be skills and tools to support industry-specific documents such as health records and construction documents. Others will support multimedia file types and provide metadata extraction and search capabilities. “Enterprises are asking agents to reason across contracts, images, PDFs, and video, and this is where most data fabrics break,” says Dave Shuman, chief data officer at Precisely https://www.precisely.com/ . “Multimodal data must be chunked, embedded, and governed with the same rigor as structured data, including lineage and access controls.” Several other emerging capabilities include: As more companies depend on AI agents in their operations, expect top data fabric platforms to release capabilities to expand scope, scale, use cases, and governance.