Systems | Development | Analytics | API | Testing

The promise of test automation was simple: automate repetitive testing tasks, catch bugs faster, and ship quality software at scale. Yet for most development teams, that promise remains unfulfilled. Traditional test automation frameworks demand specialized coding skills, require constant maintenance when applications change, and create bottlenecks that slow down release cycles rather than accelerate them.

AI can now generate SQL, build dashboards, and answer questions in plain language. But generating queries isn’t the same as understanding a business. The model might not know which revenue definition finance approves, how your fiscal calendar works, or which fields require restricted access. As AI agents become the front door to analytics, the real challenge isn’t query generation; it’s semantic grounding. That’s where the Agentic Semantic Layer becomes essential.

SwiftUI Previews show us how our app will look out in the wild and let us make changes in real time, without emulators. But that’s not the full story. The full benefit of SwiftUI Previews lies in declarativeUI, which allows us to dictate the final state we want to achieve and handles all the process stuff itself. This is a game-changer for developers, allowing us to shift our focus from ‘how’ to ‘what’.

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?

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.

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.