Qlik has made a series of extensions to its portfolio of analytics and integration technologies to make it possible to use natural language to declaratively create and manage data pipelines that will be increasingly relied on by artificial intelligence (AI) agents to automate a wide range of workflows.
Additionally, later this year Qlik will add a context-sensitive AI Assistant to its Talend Studio integrated development environment (IDE) that will make it simpler to use natural language to request help, generate jobs, create documentation, and write SQL code. Qlik is also extending its data products capability to create reusable sets of data, such as contracts, that can be more readily consumed by AI agents.
At the same time, Qlik is expanding Talend Studio to support real-time message routing for complex agentic AI workflows via its Model Context Protocol (MCP) server, which takes advantage of larger context and memory handling capabilities.
Qlik has also added agents for prediction, automation, and analytics development to an existing portfolio of Qlik Answers and Discovery Agents that are all integrated via its MCP server. The company has also added a Qlik Agentic Advisory service to help organizations build workflows.
An Open Lakehouse provided by Qlik has also now been extended with native streaming support so teams can unify continuous event data with batch and change data capture (CDC) workflows.
Finally, Qlik announced a data integration alliance with ServiceNow, launched an AI Sovereignty Initiative and added support for additional regional clouds and the European Sovereign Cloud service from Amazon Web Services (AWS).
At the core of the Qlik portfolio is Qlik Answers, a framework that combines structured data analytics and unstructured content in one governed workflow. Qlik has also extended that capability with a semantic layer that end users, third-party applications and AI agents can invoke. Those AI agents include, for example, a discovery agent capable of monitoring specific data, detecting anomalies and other significant changes, and generating alerts for further investigation.
As AI continues to evolve, Qlik will continue to add additional agentic capabilities, says Nick Magnuson, head of AI for Qlik. For example, the company has already committed to adding AI agents to the framework it launched for automated data pipeline management. In general, AI is already rapidly transforming analytics. The challenge is making sure the right data is surfaced at the right time to ensure the best possible outcomes in a way that is easily accessible, notes Magnuson. “The stuff that we’ve been working on with regard to agentic AI makes engaging with your data increasingly a lower threshold in terms of the technical aptitude,” he says.
Regardless of approach, there is now a much greater appreciation for data management in the AI era. Unlike humans, an AI agent is likely to act on the data it discovers without considering its actual veracity. That tendency to act first can in turn create a series of cascading downstream business issues that could have profound consequences. Preventing that from occurring requires a governance framework that is embedded within an integrated data engineering and analytic framework, says Magnuson.
Hopefully, there will come a day soon when dashboards are automatically generated with help from AI. In the meantime, however, IT teams still need to make sure that data is carefully curated in a way that AI agents can safely consume.
