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…
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…
Is everything fine or is AI replacing software engineers? There is lately often medialized decrease of software engineers in the last few years. You might be wondering if the fears…
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…
By 2028, 60% of software engineering teams will use AI evaluation and observability platforms – up from just 18% in 2025. The future of software engineering is rapidly evolving, and…
The adoption of AI in the engineering world has reached a boiling point. We’re not talking about early adopters or pioneers – we’re talking about the norm. Around 90% of…
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…
Resilience engineering is crucial for LLM reliability in production environments. This involves handling various types of failures, such as rate limits, timeouts, and model-specific errors, to ensure a seamless user…
Semantic search over local Markdown documentation is a common backend requirement. Traditional tools like grep work well for keyword matching, but they fail when the query requires intent understanding. This…
