Systems | Development | Analytics | API | Testing

The pitch was simple: let AI write your code so you can focus on the hard problems. Three years into the AI coding revolution, and developers are focused on hard problems alright, just not the ones anyone expected. Instead of designing systems and solving business logic, engineers in 2026 spend a startling amount of their day managing the AI itself. Should you use Fast Mode or Deep Thinking? Haiku or Opus? Cursor or Claude Code or Windsurf? Should you write a SKILL.md file or a custom system prompt?

Your tests are not flaky. Your test data is. That 401 Unauthorized that fails every Monday morning? The OAuth token in your test fixture expired 72 hours ago. The order_id that works in staging but not in CI? It was hardcoded six months ago and the format changed from integer to UUID in January. The timestamp assertion that passes at 2pm and fails at midnight? You are comparing a hardcoded 2026-01-15T14:30:00Z against Date.now(). These are not test infrastructure problems. Retrying them will not help.

Enterprise interest in generative and agentic AI has accelerated dramatically over the past two years. Organizations across industries are exploring how AI agents, intelligent assistants, and automation can improve productivity, streamline operations, and unlock insights from growing volumes of enterprise data. Yet as enthusiasm grows, so do questions around cost, security, and operational complexity.

In 2025, Model Context Protocol (MCP) captured the imagination of the AI engineering community. 2026 will be the year that everyone realises their prototypes need to run in production.

You deploy a web app. Users open it. Something breaks. It could be a button that doesn't respond on Safari. A form that submits twice on slow connections. A page that loads fine for 10 users but crashes for 500. These aren't rare edge cases. They're what happens when testing gets skipped, rushed, or treated as a final step before launch. It's not one activity. It's a system of checks that runs across the entire development lifecycle, from the first commit to post-deployment monitoring.

Modern SAP customers often face competing demands. While navigating the routine complexities of an SAP system, they must also prepare for faster releases and looming S/4HANA deadlines, juggling the day-to-day with long-term innovation. Intelligent quality assurance helps SAP users balance these priorities.

The EMEA region is undergoing a massive transformation, driven by companies demanding instant, actionable insights embedded directly into their applications and workflows. This fundamental shift away from legacy BI has translated into record-breaking momentum for ThoughtSpot, positioning EMEA as our fastest-growing region globally. The Agentic Analytics revolution is here, and ThoughtSpot is delivering on the promise to make the world more fact-driven.

When executives talk about improving logistics performance, the conversation often circles around the same three goals: speed, cost efficiency, and reliability. Yet the reality on the ground tells a different story. Traffic congestion, rising fuel costs, driver shortages, changing customer expectations, and unpredictable disruptions continue to make route planning one of the most complex operational challenges in logistics. Now add one more pressure point: customer expectations have fundamentally changed.