Introducing Conversational Analytics in BigQuery
Conversational Analytics in BigQuery in preview brings a sophisticated AI-powered reasoning engine directly into BigQuery Studio.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
How an AI Assistant Can Work With Your Business Data with MCPs
And instead of getting a generic answer or being told to check your dashboard, the AI pulls the exact numbers from your company’s data and gives you a real answer in seconds. This is no longer science fiction. A new technology called MCP (Model Context Protocol) makes this possible. It’s a standardized way for AI tools to securely connect to your business intelligence and analytics platforms and actually work with your real data.
AI, Three Years In: Speed Is Easy. Trust Is Not.
In November 2023, I flew to Germany to speak at Agile Testing Days, one of the leading software testing conferences. My keynote was called “10x Software Testing.” I knew there would be skepticism in the room.
Comparing the top AI test automation tools
AI is reshaping test automation fundamentals. Features that once required hours of manual scripting can now adapt automatically to UI changes, generate realistic test data on demand, and help teams predict which tests matter most. For QA engineers evaluating automation platforms, understanding how AI capabilities differ has become essential. This comparison examines SmartBear TestComplete, Tricentis Tosca, and Ranorex through their AI-powered features.
Refactor Safely with AI: Using MCP and Traffic Replay to Validate Code Changes
So as software engineers using AI coding assistants, we’re quickly learning of a new anti-pattern: Hallucinated Success. You give your agent (e.g. Claude via terminal or various IDE code assistants) the command “refactor the billing controller.” The agent happily complies, churning out nice clean code. The agent even goes so far as to write a new unit test suite that passes at 100%. You integrate it. Your test suites pass. Your production code breaks. Why?
Why orchestrators become a bottleneck in multi-agent AI
Complex user tasks often need multiple AI agents working together, not just a single assistant. That’s what agent collaboration enables. Each agent has its own specialism - planning, fetching, checking, summarising - and they work in tandem to get the job done. The experience feels intelligent and joined-up, not monolithic or linear. But making that work means more than prompt chaining or orchestration logic.
Qlik: Making Data Work for AI
AI is moving fast, but outcomes still depend on one thing: trusted data, in the right place, at the right time, with the right controls. In this short Qlik story video, you’ll see how we help teams accelerate AI with confidence, turning data into answers you can explain, and actions you can stand behind. From strengthening supply chain decisions, to building a campaign plan in seconds, to spotting changes as they happen, Qlik connects analytics, automation, and governed AI experiences, so AI becomes operational, not experimental.
Making Data Work for AI
AI is not a pilot anymore. In 2026, it is the operating agenda. And if you’re leading a business or an IT project right now, you’re probably getting the same two questions. First: “When do we see real outcomes?” Second: “Can we trust what we’re getting?” Those are fair questions. They’re the right questions. Because the truth is, the model is rarely the problem. The hard part is everything around it. The data. The access. The silos. The controls.
Building for Agentic AI
Our customers’ worlds are complex, and for good reason. It’s multi-cloud. It’s SaaS plus on-prem. It’s Snowflake, Databricks, AWS, Azure, Salesforce, and more. Underneath every one of those choices is the same constraint: data must be accessible, stay current, and stay controlled. The hard part is getting trusted data where it needs to be, when it needs to be there, with the controls to use it responsibly.
AI Is About to Crush Your Database!
AI agents won’t just increase traffic—they’ll overwhelm legacy databases unless core data architectures evolve to handle machine-scale demand.