Systems | Development | Analytics | API | Testing

The Model Context Protocol (MCP) has taken the internet by storm by rapidly becoming the standard for Large Language Models (LLMs) to communicate with external data sources or tools. MCP provides a structured way to fetch data and trigger workflows through APIs and functions. However, with great power comes great responsibility.

Let’s get straight to the point—this post is vital and couldn’t have come at a better time. As QA professionals, we’ve always been the gatekeepers of software quality. But with the rise of AI and LLMs, our role is evolving. Writing evaluations—assessments of AI systems—is quickly becoming a core skill for anyone working with AI products, and soon, this will include nearly everyone in tech.

Organizations everywhere are in hot pursuit of competitive advantages, seeking out and implementing artificial intelligence technologies ranging from GenAI to sophisticated machine learning systems. Yet, despite massive global investments that are projected to reach $375 billion in 2025, many enterprises remain disappointed with their AI initiatives’ real-world results. Why is it that so many AI projects are failing to deliver on their promise? The answer isn’t in the algorithms themselves.

Teams are spending as much as 71% of their time on administrative tasks and manually entering data. But what if there was a way to automate all their repetitive work so they could focus on performing higher-order tasks, creating value, and driving actual ROI? That’s what AI agents can do for you.

With Deloitte predicting that 25% of companies using Generative AI will launch agentic AI pilots or proofs of concept in 2025, is your testing strategy ready for the agentic revolution? This highlights the pace at which the modern software development industry, already demanding continuous operational speed improvements, heightened efficiency, and superior product quality, is turning to advanced AI.

AI has become the talk of the town nowadays, right? There are tons of AI tools available for different tasks, and new advancements are coming up daily like vibe coding. But how do you actually do vibe coding? Or how do you try out these models? You could use tools like ChatGPT or Claude, but they come with restrictions, and you often need to pay to access full features. What if you don’t want your data to become part of their training models? That’s where open source coding models come in.

AI agents are all the rage these days. Poised as the next big thing after Gen AI…is there substance underneath all the hype? The answer is a resounding yes. For instance, the 2024 State of AI Agents report revealed that 51% of AI professionals are already using AI agents, while 78% of enterprises and mid-sized companies have active plans to put AI agents into production. However, doing this successfully requires paying attention to certain key factors.

The workplace is on the brink of a transformation unlike anything we’ve seen before. With the rise of AI agents—autonomous software entities capable of executing tasks, making decisions, and even optimizing workflows—the way we define work itself is evolving. While automation and AI-assisted processes have been gradually reshaping industries, the concept of the agentic enterprise takes this a step further, shifting from AI as a tool to AI as an active participant in business operations.

Even in light of recent return-to-work mandates, it’s clear that the way we work has changed. Remote and hybrid teams are now the norm, and while this shift has brought flexibility, it’s also introduced unique challenges for AI and ML teams. One of the most pressing issues is ensuring seamless access to the compute resources needed to run machine learning workloads.