Systems | Development | Analytics | API | Testing

Open Data Infrastructure: Built for agentic AI

As AI accelerates the pace of change, demanding fresher data, diverse formats, and support across multiple engines, many teams discover their infrastructure was built for reporting, not real-time AI at scale. Open Data Infrastructure is redefining how organizations design for analytics, operations, and AI. By leveraging Fivetran as an interoperable data foundation, organizations can embrace open standards, separate storage from compute, and keep data portable across clouds and engines, preserving adaptability while scaling AI and operational workloads with Databricks.

From Backlog to Breakthrough: Inova Scales Data & AI with Fivetran and Databricks

Healthcare organizations operate some of the most complex data environments, spanning thousands of systems across clinical, financial, and operational domains. At Inova Health, this complexity created an opportunity to rethink how data could better support analytics and AI at scale.

Automating the Embodied AI Pipeline: A ClearML and Dell Robotics Proof of Concept

Training models for physical robots is harder than training a typical model. The data has to be collected by hand through teleoperation, every change has to be tested on real hardware, and the loop from data to deployment runs constantly. In a recent proof of concept with a Singapore government agency, ClearML, Dell Technologies, and Hugging Face’s LeRobot framework turned that high-touch, manual process into an automated pipeline.

REPLAY: Stop Mocking APIs Manually | Use Digital Twin Sandboxes and Find Regressions in CI Quickly

Your developers — and your AI agents — need a safe way to test against production-like behavior. Keploy records real API traffic and replays it as a digital twin sandbox, so you can catch regressions before they ship. No manual mocks. No production access. No complex test environment setup. Record → generate tests and mocks → replay in CI.

Spotter Memory: How Your AI Analyst Learns Your Business

You ask your agent a question. The answer is slightly off. You point out the gap. Spotter fixes it, and that fix doesn't disappear when the session ends. Your team doesn't re-explain the same thing tomorrow. The next analyst doesn't start from scratch. The correction stays, and the work gets better from here. That's what memory makes possible. Not just for you. For everyone who comes after.

Enterprise-Grade MCP Access Control Is Here. Your Gateway Makes It Real.

*Kong makes every MCP client and server work with Enterprise-Managed Authorization, whether they speak the protocol or not.* The MCP demo impressed the room. Then someone asked how 5,000 employees would connect to 40 MCP servers, and the answer was: one OAuth consent screen at a time. Per user. Per server. No central policy, no unified audit trail, and nothing stopping a personal account from getting wired into a work tool.

Introducing AI Transport v0.4.0

AI Transport v0.4.0 includes changes to optionally support database hydration. Some applications may wish to store AI conversation history in an external store, such as a database. AI Transport's support for database hydration allows applications to reconcile that stored history with the live activity in the AI session. When using database hydration, your application persists messages for completed runs to the database.

Human Testing vs. AI Testing: Striking the Perfect Balance for Flawless Digital Experiences

Twenty years of boots-on-the-ground testing experience reveals a clear pattern: the industry has moved from tracking manual test cases in Excel sheets, to managing Selenium Grid configurations, to watching algorithms generate scripts in seconds. Right now, if you are in a managerial role, your feeds are absolutely flooded with pitches promising that.

MCP vs REST APIs for Data Integration: When to Use Each

Your data integration team just asked: "Should we use MCP or REST APIs?" The answer is yes to both. With the ETL market reaching $10.24 billion in 2026 and projected to grow to $21.25 billion by 2031, understanding when to leverage each technology determines whether your AI agents can autonomously adapt to changing data needs or require manual code updates for every new integration.

How to Connect Your Data Warehouse to AI Agents With MCP

Your organization invested heavily in a data warehouse, yet business users still wait days for answers to simple questions. The disconnect between where data lives and who needs it remains one of the persistent challenges in enterprise analytics. With 95% of AI pilots failing due to poor data foundations and accessibility issues, companies need a standardized way to connect AI agents to their existing data infrastructure.