Qlik® announced an expansion of its agentic execution strategy into data engineering, introducing capabilities designed to help data teams create, evolve, and deliver trusted data faster as demand for AI-ready data continues to rise.

The pressure on data teams has changed. They are being asked to support more AI initiatives, move faster on new data demands, and do it all without adding fragility, backlog, or unnecessary cost. In many enterprises, the constraint is not ambition. It is the amount of manual work still required to build pipelines, maintain transformations, troubleshoot issues, and keep data current enough to support decisions and automation.

Qlik’s latest data engineering release is designed around that reality. It brings agentic execution into the engineering workflow itself, giving teams new ways to translate intent into working data assets, reduce repetitive effort, and speed delivery without stripping away the control required for production environments.

“Most companies do not struggle to imagine AI use cases. They struggle to deliver the trusted, current data those use cases depend on,” said Mike Capone, CEO, Qlik. “As demand rises, data engineering becomes the critical path. Qlik is helping teams reduce friction, protect trust, and keep pace with the business.”

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What’s new

• Declarative pipelines: Qlik is introducing a more natural-language-driven way to help data engineers create and evolve pipelines in context with the pipeline canvas, offer next-step guidance, and lower the barrier to building trusted flows. This release also establishes the path toward broader Pipeline Agent capabilities over time.
• AI Assistant for Talend Studio: A new context-sensitive AI Assistant inside the Talend Studio IDE, planned for later this year, is designed to help developers request help, generate jobs, create documentation, and write SQL using natural language, shifting engineering work from manual coding toward higher-level orchestration.
• Real-time routing for agentic data flows: Qlik is expanding Talend Studio to support real-time message routing for agentic processes, helping data engineers work with large language models, build domain-specific RAG pipelines, and connect agentic systems through MCP components. The latest release also expands context and memory handling to support more complex enterprise-scale workflows.
• Open Lakehouse Streaming: Qlik has extended its Open Lakehouse with native streaming support so teams can unify continuous event data with batch and CDC workloads in one environment, reducing the need for separate tooling and helping keep AI and analytics closer to current business conditions.
• A more complete engineering path for agentic workloads: Across declarative pipelines, real-time routing, Open Lakehouse Streaming, Talend Studio AI assistance, and the broader path toward Pipeline Agent capabilities, Qlik is positioning data engineering as a more intent-driven, agent-assisted function, one designed to reduce reinvention and help teams spend more time on architecture, design, and business impact.

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Taken together, the capabilities available now, along with the broader agentic experiences planned ahead, are designed to help data teams move from manual pipeline assembly toward a more agent-assisted operating model — one where engineering work becomes easier to initiate, easier to evolve, and better able to keep up with the freshness and reliability demands of AI.

“There is a big difference between an assistant that helps write code and a system that actually helps a data team move faster end to end,” said Robin Astle, Principal Developer, Valpak. “The interesting part of this announcement is the focus on pipeline creation, data quality, metadata, and stewardship together, because that is much closer to how real engineering work happens.”

This announcement is part of a broader set of releases at Qlik Connect 2026, Qlik’s annual customer and partner event, focused on agentic analytics, operational trust, sovereignty-ready deployment, and the data capabilities required to support AI under real-world conditions.