Qlik announced the general availability of new agentic data engineering capabilities across Qlik Cloud, moving the capabilities introduced at Qlik Connect 2026 into production for customers. The release helps data teams use purpose-built AI agents and declarative workflows to find trusted data, define business meaning, evaluate quality, shape data products and create pipelines through the tools they already use, so governed data can move faster into analytics, automation and AI workflows.
As enterprises move AI from pilots into business workflows, data engineering has become a critical constraint. AI agents can recommend, generate and act faster than traditional data pipelines can be built, governed or maintained. That creates a new requirement for data teams: scale delivery without giving up quality, lineage, stewardship, or architectural choice.
Qlik’s agentic data engineering capabilities are designed around that requirement. They bring AI assistance into more of the data engineering workflow, not just code generation, so teams can move from intent to trusted data products faster while keeping humans in control of the decisions that matter. Those data products can then be reused across analytics, automation and AI use cases rather than recreated for each project.
“Organisations are using many AI tools, it isn’t just one assistant or one model or a single data platform,” said Drew Clarke, Executive Vice President, Product and Technology, Qlik. “Our approach is to bring governed Qlik context into the tools data teams already use, so they can accelerate engineering work with agents while preserving choice, transparency, and control.”
What’s new
- Data Quality: Agents helps users retrieve trust scores and data quality metrics, create or edit data quality rules, define service-level objectives, run calculations, and detect or report anomalies through natural language or MCP-enabled workflows.
- Data Products: Helps teams create, manage and govern trusted data products, making curated, AI-ready datasets easier to specify, maintain, and consume across analytics and AI use cases with agentic AI.
- Catalog Glossary: Help users discover data assets, standardise terminology, and connect business definitions to governed metadata, reducing ambiguity for data teams, analytics users and AI systems.
- Declarative Pipelines with Coding: Lets data engineers work with approved third-party coding agents and development environments to generate and modify pipelines using governed pipeline context.
- Expanded MCP-enabled data tools: Gives authorised AI clients access to Qlik capabilities and context, helping teams use their preferred AI assistants while maintaining enterprise controls.
Together, these capabilities help organisations address one of the biggest obstacles to AI value: the gap between ambition and data readiness. For AI agents and analytics teams to operate reliably, they need timely data, consistent meaning, quality signals, lineage, and policy controls. Qlik brings those elements into the data engineering workflow so teams can accelerate delivery of trusted data products for AI, analytics and automation without turning governance into an afterthought.
“Enterprises are under pressure to operationalise AI faster, but many are discovering that data engineering and governance remain major bottlenecks,” said Stephen Catanzano, Principal Analyst, Data & AI, Omdia. “What’s notable about Qlik’s approach is the focus on embedding agentic capabilities directly into governed data workflows, helping organisations accelerate delivery of AI-ready data products without separating speed from oversight.”
The release also reinforces Qlik’s open approach to enterprise AI. Rather than forcing customers into a single data platform, assistant, or cloud architecture, Qlik is designed to work across the systems customers already use. Data teams can use Qlik Talend Cloud and Qlik Cloud Analytics, Qlik’s agentic experience, MCP-enabled tools, and third-party assistants to deliver trusted data while preserving flexibility as AI strategies evolve.
“Qlik’s agentic data engineering capabilities will help us find the right assets, understand quality, and move trusted data products into use faster, while keeping our governance process intact,” shared Robin Astle, Head of Qlik Analytics, Valpak. “That balance of speed and control is what will make AI practical for us.”
The announcement builds on Qlik’s broader AI strategy across analytics and data integration, including the introduction of Qlik Predict Agent and Qlik Automate Agent in June and the planned availability of Qlik Analytics Agent in Q3 2026. Qlik agentic data engineering capabilities are generally available beginning today in Qlik Talend Cloud and Qlik Cloud Analytics. Availability may vary by capability, region, entitlement, and deployment configuration.
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