Qlik connects trusted, governed data and analytics with AI agents from Amazon Quick for trustworthy decisions and results.
During the first wave of generative AI, inaccuracies were mostly frustrating (or, at worst, embarrassing). An AI hallucination might waste an employee’s time, send someone down the wrong path, or even let factual errors slip into presentations or reports.
But those inaccuracies largely didn’t set off chains of actions that could ripple through the business.
The stakes are entirely different for AI agents. Unlike generative AI tools that primarily create content or answer questions, agentic AI tools can perform multistep tasks, interact with enterprise applications, and take actions on users’ behalf. Organizations must therefore ensure the data these agents access is reliable.
Right now, many organizations struggle to get their data in order. Enterprise data is often siloed, outdated, and inconsistent, and it often lacks the context AI agents need to interpret information, as well as the governance controls needed to ensure agents handle only the information users are authorized to access.
Getting data ready for agentic AI can be time-consuming and costly. But organizations that have already invested in governed, contextual analytics don’t need to start from scratch. They can build on the environments they already trust, giving AI agents access to the reliable data, business context, and robust controls they need to reason and act effectively.
Trusted Intelligence + Agentic Action
Qlik and AWS are addressing the agentic AI data challenge by connecting Qlik Cloud Analytics with Amazon Quick through the Qlik Model Context Protocol (MCP) Server™.
This integration allows Amazon Quick’s AI agents to access Qlik’s trusted data, contextual analytics, and interoperable AI capabilities directly inside its agentic workspace. Qlik ensures data quality, builds connections across the entire data landscape, and gives Amazon Quick’s agents the context they need to go beyond simple answers (even surfacing discoveries that users didn’t initially think to ask for). Amazon Quick then puts that intelligence into users’ hands through a conversational, agentic experience.
In short: Qlik provides the trusted intelligence layer that grounds Amazon Quick’s agentic workflows in high-quality, governed data. The result is deeper answers, more valuable discoveries, and decisions users can trust.
This addresses a major challenge leaders face as their organizations enter the agentic era. The Futurum Group’s 1H 2026 AI Platforms Decision Maker Survey found that only 13.3% of organizations have reached the most advanced AI maturity stage, and even within that leading group, legacy-system integration and workforce readiness, not model capability, separate them from the rest. Futurum notes that agentic AI pilots are stalling on integration and unproven ROI rather than on the agents’ actual capabilities.
Scoring Trust for Real-World Processes
One way to help AI agents make more reliable decisions is to give them (and users) more visibility into the quality of the data they access. Qlik surfaces a numerical Trust Score ranging from 0 to 5 directly in Amazon Quick, which is the combined result of measures including completeness, accuracy, validity, and other data-quality metrics.
Here’s what this looks like in practice: A sales representative with a decade of field experience will have their work and institutional knowledge scattered across multiple solutions. Instead of switching between tools and browser tabs to complete every task, they can rely on Amazon Quick as an AI-powered daily companion, with Qlik grounding every decision in trusted data. For example, a representative might investigate anomalous sales figures using Amazon Quick’s agentic workflows, while Qlik verifies data quality.
Or consider an analytics engineer who bridges business and IT teams, building a foundation so users in those departments can act on insights. Here again, the integration between Qlik and Amazon Quick can not only surface important findings, but also automatically trigger downstream actions, alert stakeholders, update records, or kick off remediation workflows.
A Webinar to Learn More
During a recent webinar, now available on demand, we discuss these issues in more depth.
During this discussion, experts diagnose what’s currently holding back enterprise agentic AI and dig into what it takes to close that gap. We examine how governed, quality-scored data fundamentally changes what AI agents can safely accomplish; why associative, in-memory analysis surfaces context and exceptions that query-based approaches miss; and how enterprises can preserve existing access controls as they adopt agentic AI.
Key takeaways include:
- Why most AI programs stall on the data layer (rather than the model), and how to determine whether that’s what’s happening inside your own organization.
- How data quality metrics can be surfaced directly inside agentic conversations, without separate business intelligence tools.
- Why associative analysis surfaces hidden correlations and exceptions that single-query retrieval typically misses (and how this leads to faster, more confident decisions).
Register here to watch and learn more.
