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Your AI investment has a governance gap, and it's called testing

Sep 1, 2026 By Klaudia Makiej In SmartBear

Article Summary: Most teams have adopted AI coding tools, but testing is still manual, so the speed gain rarely survives to release. This post covers why that gap forms, how your team can maintain application integrity, and how QMetry’s AI features, from fast test creation to a Release Readiness Advisor, connect coverage, risk, and release decisions in one system instead of a second disconnected tool.

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Where agentic AI is most valuable in performance testing

Sep 1, 2026 By Jason Secola In Tricentis

Quick summary: Performance teams can generate tests in minutes, but the analysis still takes hours. Agentic Performance Testing in NeoLoad uses domain-specialized AI agents to deliver a finished analysis from a single request, so engineers can start with the conclusions, rather than the raw data. Performance testing answers a critical question in the quality engineering lifecycle: will this hold up when real people use it, under real conditions, at real volume?

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AI Optimization - Semantic Understanding - Quick Demo

Sep 1, 2026 By Qlik In Qlik

AI Optimization is a workspace for managing how Qlik Answers understands an application. It brings semantic management into one experience, where you can review AI-generated semantic understanding and make corrections before they reach an answer. The result is a visible, correctable layer where there used to be none. AI Optimization is the central place to manage how Qlik Answers interprets your application. Semantic Understanding, inside it, shows and lets you edit this interpretation of each field and master item.

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Your AI Feature Shipped. Now, Will Users Trust It?

Aug 31, 2026 By Jillian Heller Foreman In Testlio

The question product and engineering teams ask before shipping an AI feature is usually simple: does it work? Is the model accurate enough? Are the outputs reasonable?

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Agentic AI Just Rewrote the Data Engineer's Job Description. Here's What IT Leaders Need to Know.

Aug 31, 2026 By Dan Potter In Qlik

Gartner predicts that 70% of today's data engineering tasks will be fully automated by 2030. I put that number to Tim Garrod, Qlik's Head of Product Management for data integration and quality, on a recent Qlik Insider session, and his answer is the one every CIO, CDO, and VP of IT should sit with: automation doesn't make the data engineer obsolete, it makes the good ones ten times more valuable. AI amplifies skilled judgment. It doesn't replace it.

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New MCP tools for Declarative Pipelines

Aug 31, 2026 By Qlik In Qlik

Qlik's MCP server just got three new lookup tools built for data engineering. They connect directly to your Qlik Cloud tenant, so coding agents can pull the real project values a pipeline needs instead of working from an empty template, find spaces and data connections by name, and browse the tables and views available on a connection, just by asking in natural language. That means easier declarative pipeline creation, with real tenant context built right into your prompt for faster, more accurate iteration.

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Smarter, faster CI/CD for the new AI-powered development loop

Aug 31, 2026 By Naveen Nazimudeen In Bitrise

Most AI coding tools run in a Linux container somewhere. Codespaces is Linux. Copilot’s coding agent works in an ephemeral, Actions-powered Linux environment. Nearly every agent framework assumes a container that spins up in seconds. None of them can build your iOS app, or have build cache to speed up the builds, or have simulators that can run tests that you can view.

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What's Driving the Great AI Re-Architecture?

Aug 31, 2026 By Cloudera, Inc. In Cloudera

As enterprise AI scales, traditional data architectures are reaching their limit. Workloads are becoming more distributed, data movement is accelerating, and tech leaders face growing pressure to justify AI spend while delivering real business impact. Chief Technology Officer Sergio Gago breaks down key findings from Cloudera’s latest global survey of enterprise architects, data architects, and cloud leaders.

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AI Chatbots: Transforming Performance Testing

Aug 30, 2026 By Chris In LoadFocus

Until recently, performance testing workflows meant complex scripting, manual maintenance, and slow feedback. Now, the adoption of AI chatbots in QA and DevOps is prompting a fundamental shift. Teams using AI-driven testing tools are seeing significant reductions in test cycle times and improvements in defect detection. These are not incremental improvements, but shifts that are redefining benchmarks for speed and coverage.

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[AgentSpot Showcase Series] Winny - GTM Intelligence Agent

Aug 28, 2026 By ThoughtSpot In ThoughtSpot

Meet Winny, a GTM Intelligence agent built with AgentSpot and ThoughtSpot. See how teams can get faster answers to questions about conversion and pipeline velocity by simply asking questions in AgentSpot or Slack, with verified data pulled directly from ThoughtSpot. What is AgentSpot? AgentSpot is multiplayer AI for your business. Anyone can build, share, and collaborate with AI agents connected to your company’s data, context, and tools.

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