CData’s latest Connect AI update targets a practical obstacle to putting AI agents into production: giving them governed access to business data spread across enterprise systems. Connect AI is a managed Model Context Protocol (MCP) data-access platform—not an AI model—and the changes CData describes center on broader connectivity, more control over how agents query data, and added identity governance.
What CData Connect AI does
MCP is a protocol for connecting AI applications to tools and data. Connect AI sits between an AI assistant or agent and an organization’s business systems, exposing data and operations through MCP. CData describes the platform as providing connectivity, source-level context, and controls that preserve existing access boundaries. Those are vendor claims about the product’s design, not independent validation of its performance. CData’s Connect AI product page describes the current offering.
The problem it aims to address is the integration work needed to make enterprise data usable by AI. CRN reports CData research finding that 53 percent of organizations rely on custom-built APIs, connectors, and data pipelines to support enterprise AI, while 71 percent say AI teams spend more than a quarter of implementation time on data-integration chores. The CRN article does not identify the study’s year or methodology, so the figures should be read as CData-reported research rather than independently verified industry-wide measurements. CRN’s coverage reports the findings.
What changed in the latest offering
More connected systems and behind-firewall access
CRN says Connect AI now supports more than 350 business systems, compared with around 300 when it debuted. The update also adds Connect Gateway, which CData says reaches data sources located behind a firewall. Examples named in CRN’s report include SAP applications, SQL Server, and PostgreSQL. A larger connector count may reduce integration work, but it does not establish that every organization’s particular system, version, or deployment is supported; confirm coverage and configuration requirements with CData.
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Three types of agent tools
The update offers different levels of generality and control in the operations available to an agent:
- Universal tools: normalized operations that can work across connected systems.
- Source tools: operations specific to an individual connected source.
- Custom tools: organization-defined operations built around pre-optimized queries and explicit data-access limits.
This gives teams a choice between reusable operations and narrower, purpose-built workflows. Custom tools can constrain which query or data operation an agent is allowed to use; they do not, by themselves, demonstrate that an agent will always interpret results correctly.
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Identity and application governance
CRN reports two governance additions: SCIM 2.0 for identity lifecycle management and support for custom OAuth applications using first-party credentials. It also says queries are authenticated, authorized, and auditable. These capabilities address how identities and requests are managed; organizations still need to configure permissions and review their own access policies and audit requirements.
How the offering maps to the infrastructure gap
CData’s framing can be understood as three connected needs. Connectivity gets an agent to data sources, including some that sit behind a firewall. Context and tool design determine which operations are exposed and how source-specific information is made available. Control governs identities, authorization, and the boundaries placed around queries. Together, these address parts of the path from an AI prototype to a production deployment, but they are not a substitute for evaluating the full application, data quality, or operational risk.
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The figures on integration effort help explain why vendors are targeting this layer, but they do not prove that Connect AI will eliminate integration work or deliver a particular improvement for a given customer. Will Davis, CData’s chief marketing officer, described the challenge as connecting enterprise data to AI systems “with the right control, the right context” and accurate answers; that is the company’s perspective, not a neutral performance finding.
What to make of CData’s accuracy comparison
CData told CRN that its comparison test found 98.5 percent accuracy for Connect AI, versus 65 to 75 percent for other MCP systems. The report does not supply the benchmark methodology, sample size, exact competing systems, or independent validation. Treat the figures as a vendor-reported test result, not a general conclusion that Connect AI is more accurate than MCP products as a class.
Rank #4
Jerod Johnson, CData’s director of technology evangelism, told CRN, “If you want something to run autonomously, you need extremely high accuracy.” That is a sensible deployment concern, but the quoted comparison alone is not enough to establish how a system will perform on an organization’s own data, queries, and workflows.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What enterprise teams should check before choosing an MCP data layer
For a meaningful evaluation, compare operational fit rather than relying on connector counts or a single accuracy claim. In particular, establish:
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- Source coverage: whether the specific systems, versions, and data stores in scope are supported.
- Deployment reach: how behind-firewall sources are connected and what network or security configuration is required.
- Tool boundaries: whether teams can use shared operations, source-specific operations, or custom queries with explicit access limits.
- Identity and audit controls: how user and application identities map to permissions, how access is revoked, and what query activity is recorded.
- Evidence of accuracy: whether the vendor can explain test methodology and whether teams can validate results against their own representative tasks.
System integrators, managed service providers, and technology partners may also have an implementation route through CData’s partner program, which describes partner enablement, joint solution development, and go-to-market work. The page does not establish particular commercial terms.
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