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Stay ahead with practical insights on integrating AI and machine learning into your software lifecycle. Learn how LLMs, automation, and predictive technologies are redefining the way we build and maintain digital solutions.
Moving from AI-assisted prototyping to secure, reliable and production-ready applications. Building an application has never been more accessible. With the rise of AI-powered development tools, developers can now describe an idea in natural language and obtain a working application within hours. Tools such as GitHub Copilot, Cursor and Claude Code are changing how software is
From fragile browser automation to structured, agent-native web interactions The Browser Was Built for Humans. What About AI Agents? For more than two decades, websites have been designed around a simple assumption: a human user interacts with a visual interface. Buttons are clicked. Forms are completed. Menus are opened. Information is discovered through page layouts,
WebMCP: The Missing Interface Between AI Agents and the Web Read More »
AI governance defines how an organization should manage artificial intelligence. AI security examines how that intelligence can be compromised. Building trustworthy AI requires both. Artificial intelligence is moving from experimentation into business-critical workflows. Organizations are deploying AI assistants, integrating large language models into products, automating decisions, and introducing autonomous agents that interact with APIs, browsers,
From AI Governance to AI Security: Putting ISO/IEC 42001 and ISO/IEC 27090 into Practice Read More »
Playwright MCP vs Vibium vs Selenium + Robot Framework: A Hands-On Guide to Agentic QA AI can interact with a browser. But can it produce trustworthy test evidence? Let’s investigate. The experiment: one task, three approaches Imagine giving an AI agent access to a web application and asking it to: Log in with a valid
I Gave an AI Agent a Browser. Can It Actually Test a Web Application? Read More »
For the past few years, the software engineering conversation has been dominated by a seemingly simple question: how much code can AI generate? AI coding assistants can now produce entire functions, create test suites, explain unfamiliar codebases, refactor legacy components, and suggest fixes for complex failures. With agentic capabilities, they can also execute commands, modify
AI-DLC: The Next Evolution of Software Engineering Is About the Process, Not the Prompt Read More »
The AI model race is entering a different phase, with Agentic AI emerging. For years, comparing models meant asking the same questions to different systems. Which model writes better code, Agentic AI? Which one reasons better? Which one produces the most accurate explanation? That approach becomes increasingly incomplete when AI systems stop behaving like chat
How to Benchmark Agentic AI: Master GPT-6 and Claude 5.5 Read More »
For years, the context ai conversation was dominated by one question. That focus shaped many discussions. Which model should we use? Then came another. How do we write better prompts using context ai? Today, a different question is becoming increasingly important. What does the AI actually know about the environment in which it is supposed
How Context Becomes the Essential AI Engineering Fuel Read More »
When Software Moves Faster Than Our Ability to Prove It Works Software engineering has spent decades learning how to manage technical debt. We know what happens when architecture is neglected. We know what happens when shortcuts accumulate. We know that a system can continue working while becoming increasingly difficult, expensive, and risky to change. But
Quality Debt: The New Technical Debt Read More »
Self-healing automation promises fewer broken tests. But what happens when the test heals itself precisely when it should be failing? The Green Pipeline That Should Have Been Red Imagine this. It is Monday morning. A developer changes the checkout page. The CI pipeline runs 300 automated tests. Everything is green. 300/300 PASSED. The team is
The Self-Healing Test Trap: When Automation Fixes the Test Instead of Finding the Bug Read More »
The challenge is no longer only how fast we can build software. It is how fast we can build enough evidence to trust it. Software engineering has always been constrained by a simple reality: software takes time to build. A developer needs to understand a requirement, design a solution, write the code, debug it, review
AI-Generated Code Is Creating a New QA Problem: How Do We Verify More Software, Faster? Read More »
