Systems | Development | Analytics | API | Testing

Generative AI for QA: How SDET Workflows and Skills Are Changing

Generative AI for QA is the use of large language models to accelerate the creation and analysis of testing artifacts — drafting test cases, summarizing requirements, and generating synthetic test data. AI agents extend that capability into multi-step autonomous workflows that plan, delegate, and execute testing tasks across an entire delivery pipeline. For SDETs, the shift is not about learning to prompt more cleverly.

Human in the Loop Testing: Where AI Ends and QA Judgment Begins

The question isn't whether to use AI in QA. It's knowing exactly where to keep a human in control. The core risk: Over 75% of multi-agent failures are silent semantic errors that pass automated checks but violate business logic — detectable only by human inspection (Cemri, Pan et al., NeurIPS 2025). The division of labor: AI owns repetitive generation and execution; humans own risk analysis, requirement interpretation, exploratory investigation, and final sign-off. The operational discipline.

Agentic Analytics in Finance: Lessons from Navan and EcoLab

Finance leaders are operating in one of the most demanding macro environments in recent memory. Interest rates are moving faster than most models anticipated, reshaping the cost of capital almost overnight. Supply chain fragility has also turned working capital management into a moving target, and geopolitical uncertainty is changing how you plan for the future. Yet for many finance functions, the analytics stack hasn't kept pace with that urgency.

How Thrive Learning Scaled 56K Users with Agentic Analytics

Live from Snowflake Summit '26, tech leaders from around the globe gathered to discover how the world’s most innovative companies are making AI real for business. But few sessions delivered as much raw, practical insight as the one presented by Frankie Woodhead, Chief Product & Technology Officer at Thrive Learning. Heading up a fast-growing, £20m ARR LearnTech business that serves over 500 global customers and 5 million users, Woodhead didn't give a standard product pitch.

Ep 77 | The Rise of VibeOps: How AI Is Transforming Network Automation

For decades, network teams have been forced to choose between speed and stability. AI may finally be changing that equation. In this episode of The AI Forecast, Paul Muller sits down with John Capobianco, Head of AI and Developer Relations at Itential and author of “Automate Your Network,” to explore how AI is reshaping the future of network operations. Drawing on decades of experience in network engineering, John explains why network automation has struggled to gain traction and how AI, agents, and Model Context Protocol (MCP) could finally break the bottleneck.

How Agentic AI is Rewriting the Rules of Global Trade

Explore how a global supply chain company turned its data platform into a customer-facing product designed to operate at the speed of disruption. Boris Rabkin, Chief Information Officer at Ligentia, shares how the company executed that shift through a deliberate phased approach and a partnership with ThoughtSpot. He breaks down how to build a data foundation that scales, what it takes to embed analytics where decisions happen, and how to structure AI ownership and governance across a global regulatory environment.

How to Generate AI Test Cases in Katalon True Platform: A Step-by-Step Guide

Writing test cases sounds simple until you're actually doing it. You read a requirement, try to figure out what "done" even means for it, write out steps, realize you missed three scenarios, go back, revise, and by the time you feel okay about it, the sprint has moved on without you. This happens to every team working at any real scale. It is just what manual test case creation costs you, and it is the problem autonomous test generation was supposed to solve at a category level.

How We Used Agentic AI to Fix Kong Gateway's Flakiest Tests

Each change to Kong Gateway's codebase triggers a comprehensive test suite that runs more than 17,000 * 2 = 34,000 test cases among the two primary architectures (x86 and ARM) we support. This process takes about 23.5 hours on a single machine. But we don't wait that long. A large fleet of machines runs the suite in parallel, and we shard the work aggressively so each commit finishes in a fraction of that time. That setup works well, right up until flaky tests get involved.

Why Control Is Your New Competitive Advantage In The Age of AI | Perforce 2026

When AI Starts Shipping Code: Why Control Is Your New Competitive Advantage AI systems are already generating code, creating tests, and orchestrating infrastructure changes across enterprise software delivery. But faster execution without control creates a new kind of risk. In this webinar, Perforce CTO leaders explore why uncontrolled AI execution is emerging as one of the biggest operational risks in modern software delivery — and what leading enterprises are doing about it.