Fluent, Confident, and Wrong: Why English QA Is Not Enough for Global AI Agents
The most dangerous multilingual AI failure is a fluent output that feels native enough to trust, but is wrong enough to mislead.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Building Confidence Across APIs and AI Agents with the Swagger Contract Testing Kiro Power
There is a specific kind of confidence that comes with deploying software. Not just “the tests passed” confidence, but the kind that comes from knowing the services your application depends on still behave the way you expect them to. Preserving that integrity becomes harder as systems grow, teams move faster, and AI agents become active participants in delivery workflows.
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.
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.
Best Mobile Crash Reporting Tools in 2026 (Free & Paid)
Mobile crash reporting tools don’t just tell us when our app’s broken down. They help us pick up the pieces and build better next time. As such they play a vital role in our quest to deliver excellent user experience, so it’s important we choose the right tool for our team, users and operating systems. In this guide, we’ll compare the best mobile crash reporting tools in 2026, including Android-focused and cross-platform solutions.
Three in five businesses rely on 'gut feel' for strategic decision-making
Unable to access data-driven insights in time, 82% of UK business leaders are being forced to choose between fast and informed decisions.
Introducing Centerprise AI: The Agentic Evolution of Data Integration & Management
Astera today announced the launch of Centerprise AI, the agentic evolution of its enterprise data management platform. Centerprise AI embeds proprietary agentic harness across the full data management stack, enabling data teams to design, test, and deploy their data assets, warehouses, pipelines, data models, and analytics in a single platform.
Is Claude Mythos 5 Worth the Hype? #speedscale #claude #mythos #claudemythos #mythos5 #aiagents
Your workflow is officially outdated. Mythos 5 just changed the game. Here is everything you need to know in under 60 seconds.
Why Don't Data Leaders Trust AI? And Other Insights From Our 2026 AI Survey
Ever since AI-driven analytics burst onto the scene, product leaders have been racing to adopt it. Promoted as a way to stay ahead of the curve, AI analytics bring the promise of streamlined processes, personalized recommendations, and a more efficient user experience. But AI advancements aren’t without pitfalls, chief among them inaccuracies caused by AI hallucinations and pilot projects not making it to production.
Process: The Missing Link Between AI Agent Orchestration and Measurable Enterprise Value
AI is at the center of every conversation around operational efficiency, while at the same time being sidelined. In a recent Harvard Business Review Analytic Services survey, only 18% of organizations report that AI is integrated within most of their workflows; twice as many run it as a standalone tool alongside the work. That gap—between AI that assists and AI that operates—is the defining problem of enterprise AI agents.