Systems | Development | Analytics | API | Testing

Qlik 2026 Trends - Powering the Future of AI

Somehow, another year has passed and we’re back again to deliver our Trends Outlook for 2026. As Qlik’s first milestone of the year, it’s a busy but exciting time that hugely energizes me after the holiday break. We weren’t short of inspiration. The world has given us a far from a blank canvas on which to illustrate our Trends and in particular, conversations around AI have firmly evolved from future-gazing to wrestling with the formula for harnessing its power.

AI Agent with Strands SDK, Kong AI/MCP Gateway & Amazon Bedrock

In one of our posts, Kong AI/MCP Gateway and Kong MCP Server technical breakdown, we described the new capabilities added to Kong AI Gateway to support MCP (Model Context Protocol). The post focused exclusively on consuming MCP server and MCP tools through Kong MCP Gateway. Now, it's time to check how an AI agent can leverage the AI and MCP infrastructure exposed and protected by Kong AI/MCP Gateway.

AI in Action: Powering the Future of Testing | Xray Webinar

A quick overview of Xray Test Management - cutting-edge test management app for Jira. Xray is the leading Quality Assurance and Test Management app for Jira. More than 4.5 million testers, developers and QA managers trust Xray to manage 100+ million test cases each month. Xray is a mission-critical tool at over 5,000 companies in 70 countries, including 137 of the Global 500 like BMW, Samsung and Airbus.

Supercharge your LLM Using Production Data Context

Are your LLM coding agents (like Cursor or Claude Code) hallucinating fixes because they don't know what's actually happening in production? In this video, Matt from Speedscale shows you how to bridge the gap between your local IDE and live production traffic using the Model Context Protocol (MCP). Most observability tools just give you telemetry. Speedscale’s MCP server gives your agent the "inner workings" of actual API calls and payloads, so it can check its assumptions against reality. No more "vibe-coding" and hoping it works; let your agent find the 500 errors and rate limits for you.

How do you plan to test 10x more code with the same old tools?

You can’t test 10x more code with the same old tools. As AI dramatically increases code volume and speed, traditional testing becomes a bottleneck. Teams need AI embedded across the entire testing lifecycle to scale testing, boost productivity, and keep releases moving fast without sacrificing quality — Alex Martins, VP of Strategy at Katalon Follow Katalon for more insights in our series!

Let Your LLM Debug Using Production Recordings

Modern LLM coding agents are great at reading code, but they still make assumptions. When something breaks in production, those assumptions can slow you down—especially when the real issue lives in live traffic, API responses, or database behavior. In this post, I’ll walk through how to connect an MCP server to your LLM coding assistant so it can pull real production data on demand, validate its assumptions, and help you debug faster.

Securing LLMs: Insights into OWASP Top 10 | Maryia Tuleika | TTTribeCast Webinar

AI can feel like a black box, but when it is tested like any other system, unexpected weaknesses begin to surface. This session explores how large language models can be pushed into unsafe or unintended behavior, revealing that AI is not immune to flaws, poor decisions, or broken assumptions.