Qlik Extends Analytics from Answers to Agentic Action

Qlik today at its Qlik Connect event in Florida announced a major expansion of its agentic analytics capabilities, bringing together Qlik Answers, Discovery Agent, and MCP Server with new agents for prediction, automation, and analytics development.

With Qlik's latest agentic analytics release, Qlik Answers remains the entry point, combining structured analytics and unstructured content to deliver contextual answers. Discovery Agent monitors key data areas and helps surface important changes and anomalies. Automate Agent executes actions and workflows in downstream systems based on insight and agentic reasoning. Predict Agent builds machine learning models, generates predictions, and helps answer forward-looking questions. Analytics Agent helps teams accelerate analytics development tasks and creation workflows. And MCP Server allows third-party AI assistants to use Qlik analytics to support decisions, bringing Qlik's context-rich calculations into the assistants teams already use while helping preserve value from existing Qlik investments.

Qlik is also extending data products with shared, reusable business definitions, including measures, dimensions, and relationships, so Qlik Answers, analytics apps, and third-party assistants can work from more consistent business meaning across analytics and AI workflows.

"The bar for enterprise AI is getting much higher," said Mike Capone, CEO of Qlik, in a statement. "It is not enough to produce a fluent answer. AI;has to understand the business in context, run on a trusted foundation, and connect insight to action in the systems teams already use. That is how organizations create value without adding more fragility, lock-in, or spend."

Qlik also used the conference to introduce an expansion of its agentic execution strategy into data engineering with new capabilities to help data teams create, evolve, and deliver trusted data faster. Qlik's latest data engineering release 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," Capone said. "As demand rises, data engineering becomes the critical path. Qlik is helping teams reduce friction, protect trust, and keep pace with the business."

Included in this part of the release are the following:

• 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. 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.
• 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.

Qlik also expanded its trust and governance capabilities for AI, centered on data products and the operational controls required to make them reliable for both human decision-making and AI-driven action. This release brings together data products, trust signals, operating standards, anomaly detection, and agent-assisted stewardship into a tighter set of capabilities designed to help teams monitor, govern, and improve the data products that feed analytics and AI.

"As AI moves from answers into decisions and actions, weak data stops being a reporting problem and becomes an execution problem," Capone said. "Data products need the same accountability as any other production asset, with clear signals for what humans and AI can safely rely on. That is how enterprises scale AI without scaling risk."

The new capabilities in this part of the release include the following:

• Data Products in Qlik Analytics:Qlik is advancing data products as governed, AI-ready units of value that teams can create, manage, share, and reuse across analytics and AI workflows. This includes shared business definitions for measures, dimensions, and relationships, helping people and AI systems rely on more consistent business meaning.
• Data Product Agent: Qlik is introducing Data Product Agent to help teams create, manage, and deliver data products using natural language. It is designed to evaluate data products for quality, generate Trust Scores, and help humans and AI systems understand where to go for data and how good it is.
• Qlik Trust Score as an operational signal: Trust Score is a visible signal that evaluates data products across dimensions such as accuracy, timeliness, diversity, and completeness. The goal is to help teams inspect readiness before decisions or automated actions depend on it.
• Data contracts and operating expectations: Qlik is introducing a contract layer that helps teams define what a data product is expected to provide, giving producers a clearer operating standard and consumers a more explicit basis for trust.
• Service levels, alerting, and anomaly detection: New service-level objectives, alerting, and anomaly detection help teams monitor whether data products continue to meet expectations over time, surfacing degradation and drift before issues compound into business risk.
• Data Quality Agent for trust workflows: Qlik is extending agent-assisted operations into trust workflows with support for retrieving trust signals and data quality metrics, creating and editing rules, defining service levels, running calculations, and detecting anomalies through conversational interactions.
• AI-enabled stewardship at scale: New stewardship capabilities help teams generate rules, improve glossary coverage, create field descriptions, and recommend remediations more efficiently, reducing manual burden while keeping people in control of final decisions.

Related Articles

Qlik Partners with ServiceNow

Partnership combines ServiceNow Workflow Data Fabric with broader, governed enterprise context.

Qlik Releases Agentic Analytics and MCP Server

New capabilities in Qlik Cloud unify an analytics engine, curated content, and governed data products, while MCP extends Qlik's intelligence into leading AI assistants. 

Qlik Debuts Agentic Experience and MCP Server

Qlik Answers enhancement unifies analytics and documents with explainable, engine-backed reasoning, and MCP exposes Qlik to third-party assistants for trusted intelligence at scale