Insights into building and using AI agents that interact with tools, reason over tasks, and operate autonomously in real-world systems.
AI engineering is shifting from pure model building to system design, where integrating models into reliable backend services is the main challenge. Modern AI stacks combine Large Language Models, data pipelines, and observability, making them closer to distributed systems than…
What are the trends and challenges in agentic AI architecture? The landscape of AI is shifting towards more autonomous and goal-driven systems, leaving behind the single Large Language Model calls that dominated the past. Today, we’re witnessing a new wave…
AI agent testing faces challenges that are different than traditional software testing. Traditional testing methods often rely on predictable outcomes, but modern AI agents don’t play by those rules. Their non-deterministic outputs throw a wrench into the testing process, breaking…
Deciding whether to build an AI agent can be a puzzling task, especially when faced with the promise of cutting-edge technology. However, it’s essential to understand that AI agents are not a one-size-fits-all solution. They make sense when decisions require…
The Context Gap in AI Assisted Programming The biggest limitation of modern AI assisted programming tools is not intelligence – it is context. While tools like Claude or IDE agents can generate high-quality code, they remain blind to your local…
