The IT Nerd

CData Software today announced Foundations 2026, a free, one-day virtual event taking place November 5, 2026. Under the theme “The AI gateway, from every angle,” Foundations brings together CData’s CEO and product leaders, a customer CIO and the company’s research team to show how enterprises put AI to work inside their systems with the governance, context and cost control that production demands.

The event follows the September launch of Connect AI Gateway, one control point for every model call, tool call and system action, whether the request comes from an agent, an application or a person. Across the day, CData will show how AI reaches hundreds of enterprise systems through one governed path, how the gateway learns a company’s business from every interaction, and how leaders can roll AI out across an organization without multiplying risk.

Foundations 2026 sessions include:

• The AI Gateway Opportunity — Amit Sharma, CEO, and Raviv Levi, Chief Product and Technology Officer, CData

Where enterprise AI adoption stands, where companies need the most help, and the case for one point of context and control between AI and the systems themselves.

• The Context Engine — Dr. Nick Golovin, VP of Enterprise Data Platform; Mike Albritton, SVP of Cloud; and Amit Naik, VP of AI Architecture, CData

How the gateway learns what a company’s data means, captures its definitions and turns approved corrections into knowledge every request reuses, so answers improve with use and the context stays with the customer no matter which model runs.

• The CIO Perspective: Rolling Out AI at Anaqua — Erik Bailey, CIO, Anaqua, and Will Davis, CMO, CData

How a company that can’t afford to get AI wrong brought it into production: balancing speed with safeguards, what the rollout looked like and what Bailey would tell a CIO starting this year.

• CData Labs: Testing the Gateway in the Open — Jerod Johnson, Director of Technology Evangelism, and Amit Naik, VP of AI Architecture, CData

CData’s open research program on how gateway design changes what AI can do, covering accuracy on enterprise questions, token efficiency and model behavior across architectures, with the benchmark harnesses and raw results published for anyone to run.

The full agenda also includes a guided walkthrough of the gateway architecture with Mike Albritton and Amit Naik, and a conversation with CData’s own marketing, finance and business development teams on how they run their work through the gateway today.

Registration

Foundations 2026 is free and takes place entirely online on November 5, 2026. To view the full agenda and register, visit https://www.cdata.com/events/foundations-2026/.

CData Software today launched CData Connect AI Gateway, the next evolution of the Connect AI platform designed to give organizations a single control point for the models, tools, data and actions used by AI agents and the individuals who work with them. The Gateway connects agents and individuals to enterprise systems through governed tools, applies company context at every step, enforces permissions down to the record and routes each request to the most efficient model for the task, helping enterprises move AI from answering questions to taking action.

Built on CData’s data layer, which already powers Palantir, Google, hundreds of enterprise products and thousands of customers worldwide, Connect AI Gateway understands the schemas, objects, relationships and operations of connected systems from day one. It imports the knowledge a company already holds, including metric definitions, business terminology and existing semantic models, and keeps what resolves each request, so context accumulates inside the customer’s environment and under its control. Schema-aware tools also send models only the information a request needs, cutting AI cost while keeping AI accurate across systems.

Bringing Enterprise Context to AI Agents and Individuals

Connect AI Gateway is built on CData’s portfolio of hundreds of schema-aware connectors. The connectors give AI access to the structure, relationships and semantics of the systems they reach, including applications, databases, data warehouses, on-premises systems and legacy environments. Its design makes five things possible:

• Enterprise MCP built in, useful on day one. Hundreds of CData-built connectors exposed as governed tools over live systems the moment an agent connects: SAP, NetSuite, Salesforce, the warehouses and databases, on-premises and legacy included. One managed platform replaces the server-per-system sprawl, and MCP servers a customer already runs are governed under the same policy.
• Context that compounds, under your control. The Gateway starts from the schemas and relationships the connectors carry, adds the metric definitions, terminology and semantic models a company already has, so Finance’s “revenue” and Sales’ “enterprise segment” resolve the same way every time, and turns approved corrections into reusable context, available to every authorized agent and user inside the environment.
• Security and governance at every step of the request. Agents act with exactly the access their users have, and policy applies to the model, the tool and the data. Every request carries the identity behind it, person or agent, with entitlements enforced at the source down to the record. Changes are validated against the system’s own business rules before they commit, and one audit trail traces any figure in an answer back to the prompt, model, tool and policy behind it.
• Reduced cost at every layer where tokens accrue. Schema-aware tools filter, join, and aggregate at the source, so only the answer set enters the context window, and routing sends each request to the most efficient model that can serve it. Spend is attributed and budgeted by team, agent, model, and tool.
• No stack to steer AI toward. No warehouse to fill, no model to favor, no platform to route deeper into. Context lives in a portable graph outside any model or data platform, so frontier, open-weight and locally hosted models all inherit the same definitions and know-how—switch models, and the context arrives with it. If a model provider fails, requests can fail over to another with the context intact.

In CData Labs research, prompts run through CData’s connectors returned correct results 98.5 percent of the time across CRM, project management, data warehouse and ERP systems, against 65 to 75 percent for competing MCP approaches. A second study ran 22 models, from economy to frontier tier, against live enterprise systems. Through governed tools every model returned the same correct answer, and the least expensive did it at 178 times lower cost than the most expensive. Governed tools also kept models to the records they were authorized to change, something no model managed on raw access.

One Governed Path for Every Agent Interaction

As enterprises move beyond AI assistants and toward agents that execute business processes, organizations need visibility and control over more than the model generating a response. They need to know which systems an agent accessed, what data it used, which policies governed the interaction, and what actions it ultimately took.

Connect AI Gateway answers those questions from one place. Every agent interaction travels one governed path between the agent and the systems where work gets done: grounded in company context, checked against policy at the model, the tool, and the data, and logged from the prompt to the record it touched. Because that path is built on CData’s own connectors, organizations get this control without building and managing separate infrastructure for every system an agent reaches.

That’s what lets enterprises put agents into business-critical workflows—taking action, with data, context, security, and cost under control.

Availability

CData Connect AI Gateway is available through an early access program beginning September 29, 2026.

CData Software today announced that CData Connect AI now provides a governed AI data layer built for HIPAA-regulated environments, enabling healthcare providers, payers, life sciences organizations, and healthcare technology companies to securely connect AI applications and agents to protected health information (PHI).

Healthcare organizations are moving AI into clinical, operational, and administrative workflows, and most hit the same wall: getting AI to access and act on sensitive data while holding the security, governance, and compliance that HIPAA requires. Connect AI addresses this by providing a governed access layer that connects AI applications directly to live enterprise systems, without replicating or exposing sensitive data.

CData Connect AI provides the governance capabilities organizations need to confidently deploy AI applications that interact with PHI, including:

• Secure, credentialed access to live healthcare data without replication or unnecessary data movement.
• Comprehensive audit logging that tracks every AI query and user interaction.
• Identity-aware access controls that enforce existing user permissions.
• Support for Business Associate Agreements (BAAs) for eligible customer deployments.
• Centralized governance across AI assistants, applications, and autonomous agents.

The platform lets healthcare organizations deploy AI-powered use cases such as clinical decision support, operational assistants, patient service automation, revenue cycle optimization, and healthcare analytics while holding strict governance over sensitive data.

Unlike approaches that require organizations to move healthcare data into separate AI platforms or custom pipelines, Connect AI keeps data in place and provides a controlled layer that lets AI interact with the enterprise systems where the data already resides. CData already delivers this connectivity to regulated, audit-heavy organizations, including life sciences leader GSK, giving healthcare teams a proven foundation for governed AI.

CData Connect AI is available immediately. Organizations interested in deploying HIPAA-compliant AI workloads can learn more about eligibility requirements and Business Associate Agreements at cdata.com.

CData Software today launched three products for developers building AI applications on enterprise data: Connect AI Developer Edition (free), the CData Connect AI Python SDK (open source), and CData CLI.

The releases give developers direct, governed access to enterprise systems, Salesforce, Snowflake, NetSuite, Microsoft 365, Workday, and hundreds of others, through the interfaces they already use: SQL, Python, the command line, and MCP.

Most enterprise AI projects stall at the data layer, not because the models are wrong, but because getting governed, reliable access to production systems requires IT involvement at every step. Connect AI is what IT deploys so developers don’t have to ask for permission each time. Business teams get AI workflows. Developers get a stable data interface. IT gets visibility and control over every query.

Connect AI Developer Edition

Connect AI exposes enterprise APIs as a consistent, queryable data layer with standardized schema, read/write support, and automatic handling of authentication, rate limits, versioning, and pagination. Developers write queries. The platform handles the rest.

The free Developer Edition includes the full enterprise feature set: MCP server support, per-user authentication passthrough, query logging with user-level attribution, and a management MCP server. It works out of the box with any MCP-capable coding assistant, client, or framework, including Claude Code, Codex, Cursor, and LangChain, among others.

Connect AI also ships with Toolkits, which let teams package governed data access into a single MCP Server URL scoped to specific use cases, so agents get exactly what they need and nothing more.

Connect AI Python SDK

The Python SDK provides DB-API-compliant access to Connect AI, so developers can pull governed enterprise data into existing Python workflows without changing how they write code. It works with pandas, SQLAlchemy, cursor-based queries, and any other DB-API-compatible tool.

The SDK is open source and available now.

CData CLI

CData CLI is a command-line interface for CData’s JDBC, ODBC, Python, and ADO.NET connectors, designed to speed up development and testing for analytics pipelines, BI integrations, and ETL workflows. It’s built for how developers work today: CLI-native tooling that coding assistants like Claude Code and Cursor can use directly to scaffold connectivity without digging through documentation. The initial release supports JDBC, with support for ADO.NET, Python, and ODBC coming in future releases.

All three products are available today at cdata.com/developers.

CData Software today announced the appointment of Raviv Levi as Chief Product and Technology Officer (CPTO), along with the additions of Amit Naik as Vice President of AI Architecture and Craig Sanchez as Senior Vice President of Embedded Sales. The executive appointments support CData’s continued momentum as organizations rethink how enterprise data is accessed, governed, and acted on, whether by people working through conversational AI or by autonomous agents operating across enterprise systems.

Levi joins CData from Sift, where he served as Chief Product and Technology Officer, following senior leadership roles at Cisco, including Vice President of Security Strategy and Innovation for Cisco’s Security Business Group. He has led product, cloud security, and platform initiatives focused on enterprise-scale infrastructure and AI-driven technologies.

As Vice President of AI Architecture, Naik will lead the design and evolution of CData’s AI architecture, working across product and engineering to ensure the platform meets the technical demands of enterprise AI deployments. He joins CData from Calix and previously held senior leadership roles in AI/ML solutions and infrastructure at PayPal, Financial Engines and Oracle.

Sanchez will lead CData’s embedded sales organization, helping software vendors and platform providers integrate enterprise-grade connectivity and AI data access directly into their products. He joins CData from Vectara and previously held senior sales and business development leadership roles at Elastic and Cloudera.

The appointments reflect growing enterprise demand for AI systems, both conversational and autonomous, that can securely operate across fragmented data environments while maintaining governance, auditability, and real-time access. CData’s customers include global organizations such as Adobe, Office Depot, GSK, UiPath, and Palantir.

In a new blog post, Amit Naik, VP of Artificial Intelligence at CData, explores Anthropic’s “Claude Managed Agents” and what the concept of a “meta-harness” reveals about the next phase of enterprise AI. While much of the market focus remains on model performance, Naik argues that the real shift is happening at the infrastructure layer that enables agents to operate reliably at scale.

The post examines how managed agent platforms abstract the complexity of orchestration, memory, security, and tool integration, allowing organizations to accelerate development without building everything in-house. At the same time, Naik highlights key trade-offs, including potential vendor lock-in and reduced control over data and agent behavior.

In Naik’s opinion, managed agent infrastructure is a critical battleground for enterprise AI, where success will depend not just on model quality, but on how effectively organizations can operationalize and scale intelligent agents.

Read the full blog here: https://www.cdata.com/blog/claude-managed-agents-anthropic-meta-harness

CData Software today announced major enhancements to CData Sync designed to meet the data pipeline demands of modern enterprises. The updates deliver coordinated pipeline orchestration, expanded change data capture (CDC) for mission-critical systems, and native support for open table formats, empowering data teams to operate continuously across legacy and modern architectures.

As organizations race to operationalize AI, they face mounting pressure to keep data fresh, coordinate dependencies across systems, and maintain governance at scale. CData Sync’s latest capabilities directly tackle these challenges by unifying real-time replication, workflow orchestration, and open standards within a single platform.

Pipeline-Based Workflow Orchestration
CData Sync now includes Pipelines, enabling teams to orchestrate multi-step workflows directly within Sync. Data engineers can sequence replication jobs, transformations, and events without external orchestration tools, reducing complexity while maintaining full visibility and control over dependencies.

Programmable Control via API 2.0
The redesigned API 2.0 provides a predictable, automation-friendly interface for managing Sync at scale. Organizations can programmatically configure pipelines, trigger executions, and monitor operations across distributed deployments, making it easier to integrate Sync into internal platforms or enable orchestration through external systems, including AI agents.

Enterprise-Grade CDC for IBM DB2 and SAP HANA
CData Sync expanded CDC support to include IBM DB2 (LUW and iSeries/AS400) and SAP HANA, enabling near-real-time replication from these widely deployed enterprise platforms. Organizations can now stream incremental changes from core systems of record directly into cloud analytics and AI platforms without impacting production workloads.

Open Table Formats for AI and Analytics
With native support for Delta Lake (including Microsoft Fabric via Open Mirroring) and Apache Iceberg, CData Sync allows teams to write data into open, ACID-compliant table formats. This eliminates vendor lock-in and ensures data remains accessible across analytics engines and AI platforms without proprietary dependencies.

Centralized Governance with Workspaces
New Workspaces provide a unified control plane for managing connections, jobs, and transformations across teams and environments. As pipeline counts grow, Workspaces ensure organizations can scale governance, enforce policies, and maintain visibility without losing operational control.

Learn More

To explore CData Sync’s new pipeline orchestration, expanded CDC support, and open table format capabilities, visit cdata.com/sync.

CData Software today announced major enhancements to CData Connect AI at the Gartner Data & Analytics Summit (Booth #308). The updates extend CData’s managed Model Context Protocol (MCP) platform with new capabilities across connectivity, context, and control, the three pillars required to move AI from experimentation to production.

Why AI Stalls Before Production

AI investment is accelerating. “Gartner®¹ says worldwide AI spending will total $2.5 trillion in 2026.” But spending isn’t translating into results. Most generative AI initiatives still stall before reaching production. The bottleneck isn’t model capability, it’s the data infrastructure underneath. Without live connectivity to business systems, semantic intelligence that gives data context to AI, and governance controls that enforce security at scale, AI initiatives fail to deliver business value.

CData’s own State of AI Data Connectivity Report reinforces this reality. Only 6% of organizations are satisfied with their current data infrastructure for AI. More than half still rely on custom-built integrations that can’t scale. And 71% of AI teams spend over a quarter of their implementation time on data integration alone, time spent wiring plumbing instead of building intelligence.

Connect AI: Connectivity, Context, and Control in a Single Platform

CData Connect AI is purpose-built to address the data infrastructure gaps that prevent AI from reaching production. Today’s enhancements extend the platform across all three pillars

Connectivity: Connect Gateway and 350+ Data Sources

Connect AI provides live, read-write access to more than 350 business systems, without replication or data movement. The new Connect Gateway extends this reach to data sources behind the firewall, with support for SAP, SQL Server, and PostgreSQL, and more. The result: AI systems can operate against live data regardless of where it resides.

Context: Expanded Agent Tooling and Toolkits

AI agents need business-aware context to choose the right actions and avoid unnecessary MCP tool calls. But exposing too much context creates new risks: increased token usage, model confusion, and unintended access to sensitive data or operations. Connect AI addresses this challenge with a scoped MCP architecture that precisely controls what each agent can see and do. This release introduces three complementary tool types:

• Universal Tools provide a normalized set of operations that work consistently across all 350+ connected systems. Instead of exposing hundreds of system-specific tools, agents receive a compact, schema-aware interface ideal for data exploration, ad-hoc analysis, and multi-source reasoning — without tool surface bloat.
• Source Tools expose tightly defined operations specific to each system. These tools map directly to approved system actions, allowing IT teams to enforce predictable execution, transactional safety, and auditability for production workflows.
• Custom Tools allow organizations to define purpose-built operations tailored to specific workflows. These tools execute pre-optimized queries with explicit data access limits — reducing token usage, improving performance, and eliminating unintended data exposure.

Workspaces define the data boundary for each agent by specifying exactly which datasets, schemas, or views are accessible. New Toolkits define the action boundary by determining which Universal, Source, or Custom Tools are available. Each Workspace and Toolkit combination can be deployed as a dedicated MCP server, ensuring that agents operate only within their intended scope; reducing context noise, strengthening governance, and delivering enterprise-grade control over agent behavior.

Control: SCIM and Custom OAuth Applications

Connect AI enforces per-user authentication with native source-system permissions applied dynamically at runtime, backed by full audit trails. New governance enhancements include SCIM 2.0 for automated identity lifecycle management and Custom OAuth Applications that enable organizations to use first-party credentials to meet internal security and compliance requirements. Every query is authenticated, authorized, and auditable.

The 25% Accuracy Gap: Why Architecture Matters

MCP is becoming the default interface between AI agents and business software. But how accurately do MCP providers actually return data? To find out, CData tested five MCP providers, representing the major architectural approaches in the market, across four sources (CRM, project management, data warehouse, and ERP) using 378 real-world prompts. Every response was scored against pre-established ground truth. No partial credit.

The results revealed a significant accuracy gap. CData Connect AI achieved 98.5% accuracy (67 of 68 correct responses). The other providers ranged from 65% to 75%—failing on one out of every three to four queries. The failures weren’t random: they clustered around relative date logic, multi-filter queries, semantic interpretation of business terms, and write operations, exactly the kinds of tasks AI agents need to perform reliably every day.

For organizations moving beyond copilots toward autonomous agents that read, write, and act on live business data, this gap is decisive. At 75% accuracy, an AI agent fails one out of every four actions. And that inaccuracy compounds: 75% accuracy across a five-step workflow means less than 24% of processes complete successfully. A 75% accuracy rate becomes a 75% failure rate.

Most MCP providers translate natural language directly into API calls, which works for simple lookups but breaks down when queries require date math, multi-condition filtering, or platform-specific business logic. Connect AI uses a relational abstraction layer with semantic intelligence that understands entity relationships, business conventions, and workflow rules. That’s why it maintained near-perfect accuracy across every platform tested, including ERP, where the vendor’s own native MCP server failed completely.

View the full benchmarking methodology and results here: https://www.cdata.com/lp/ai-accuracy-whitepaper/

Organizations deploying AI in production need an accuracy rate that prevents autonomous agents from creating more cleanup work than they save. Connect AI is built to clear that bar because connectivity, context, and control aren’t just platform features. They’re what makes accuracy at scale possible.

CData at Gartner Data & Analytics Summit

CData will be at the Gartner Data & Analytics Summit at Booth #308, where attendees can connect with the team and see the latest in universal data connectivity.

Speaking Session: AI Agents and the Future of Digital Work with Microsoft — CData Chief Product Officer Ken Yagen will take the stage alongside Microsoft Partner Director of Product Management James Oleinik on Wednesday, March 11 (11:15–11:45 AM EDT). The session will present a joint blueprint for moving from AI pilots to production-ready agentic AI, exploring how Copilot Studio and universal data connectivity can deliver the governed infrastructure enterprises need as Gartner predicts 40%+ of agentic AI projects will be canceled by 2027 without the right architecture in place.

Supporting Resources

• The 25% Accuracy Gap: MCP Provider Performance Across Enterprise Workloads — CData’s benchmark of five MCP providers across 378 enterprise queries found a 25+ percentage point accuracy gap, with CData Connect AI achieving 98.5% accuracy compared to 65–75% for other providers. Download the whitepaper: https://www.cdata.com/lp/ai-accuracy-whitepaper/
• The State of AI Data Connectivity Report: 2026 Outlook — Based on research with 200+ data and AI leaders and insights from AI pioneers at Microsoft, AWS, and Google, CData’s report found that only 6% of enterprises consider their data infrastructure fully ready for AI — establishing a direct link between data infrastructure maturity and AI success. Download the report: https://www.cdata.com/lp/ai-data-connectivity-report-2026/

¹ Gartner, Inc., “Gartner Says Worldwide AI Spending Will Total $2.5 Trillion in 2026,” Gartner.com (Jan. 15, 2026), accessed Feb. 20, 2026, https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026
GARTNER is a trademark of Gartner, Inc. and/or its affiliates.

 CData Software today announced that it has been recognized in the 2025 Gartner® Magic Quadrant™ for Data Integration Tools. This marks the second consecutive year that CData has been included in the report.

The company’s unified platform delivers real-time access, semantic intelligence, and comprehensive governance across diverse data sources, empowering organizations to activate their complete data landscape for use in AI, and analytics. Guided by its vision to make data more accessible and actionable for both humans and AI, CData continues to advance innovation in data integration. Ongoing investments in AI integration are focused on addressing one of today’s most critical enterprise challenges: connecting fragmented data to AI systems to enable conversational analytics and agentic platforms.

CData continues to gain industry recognition for its innovation and momentum in data integration and connectivity. Based on real customer reviews, CData positioned again in the Strong Performers quadrant in the 2025 Gartner Peer Insights™. Other recent honors include 2025 Inc. 5000 list, the Accel 2025 Globalscape Top 100 report, The Software Report’s Top 25 Data Management and Analytics Companies of 2025, and the DBTA 100 2025: The Companies That Matter Most in Data.

Access a complimentary copy of the full report here: https://www.cdata.com/lp/gartner-magic-quadrant-data-integration-2025/

Only 6% of enterprise AI leaders say their data infrastructure is fully ready for AI: a readiness gap that has become one of the biggest constraints on AI progress. That’s a central finding of CData Software’s new report, The State of AI Data Connectivity: 2026 Outlook, which draws on independently collected survey data from more than 200 data and AI leaders at software providers and enterprise organizations. The report establishes a direct link between data infrastructure maturity and AI maturity, identifying the core capabilities that define AI-ready data infrastructure and revealing how gaps in data connectivity, context, and control are stalling AI initiatives across industries.

Infrastructure Gaps Hold Back AI Progress

The research exposes a stark divide: 60% of companies at the highest level of AI maturity have also invested in advanced data infrastructure, while 53% of organizations struggling with AI implementations are hampered by immature data systems. The gap is costing companies time, money, and competitive advantage.

Key Findings:

• AI teams are drowning in data plumbing: 71% of AI teams spend over a quarter of their time on data plumbing instead of innovation
• Connectivity complexity is exploding: 46% of organizations need real-time access to six or more data sources for a single AI use case
• Real-time data is universally critical — but still missing: 100% agree real-time data is essential for AI agents, yet 20% still lack real-time integration capabilities
• AI-Native Providers Are Outpacing Traditional Software in Integration Demands: AI-native software providers require 3x more external integrations than traditional companies (46% need 26+ integrations vs. 15%)
• Infrastructure maturity is the great divide: All high-AI-maturity organizations have built centralized, semantically consistent integration layers — 80% of low-maturity providers haven’t even started

Investment Priorities Shifting from Models to Infrastructure

The report signals a fundamental shift in AI strategy. Only 9% of organizations now rank AI model development as their top investment priority, while 83% are investing in or planning centralized, semantically consistent data access layers.

Download the full report: https://www.cdata.com/lp/ai-data-connectivity-report-2026/

About the Report

The State of AI Data Connectivity: 2026 Outlook provides benchmarks for both enterprises and software providers in two key areas:

1. Enterprise AI Adoption — How data infrastructure gaps are limiting AI success and what separates high performers from laggards
2. Product AI Strategy — How software companies are embedding AI capabilities and managing escalating integration complexity

The research references findings from the August 2025 MIT report, The Generative AI Gap: The State of Business AI in 2025.