Observability

Large Language Models (LLMs) have rapidly evolved from research novelties to foundational elements in enterprise AI applications. As organizations deploy LLM-powered agents in critical workflows, the focus has decisively shifted from mere prototyping to ensuring reliability, transparency, and continuous improvement in production environments. Observability-driven development is now essential for building

The rapid evolution of AI agents (from simple chatbots to complex, multi-agent systems) has transformed how organizations automate workflows and deliver intelligent services. However, as AI agents become more autonomous and embedded in critical business processes, robust monitoring and observability are essential to ensure reliability, compliance, and continuous improvement. In

Introduction: Beyond the Benchmark Mirage In the last decade, the AI industry has undergone a transformation that would have seemed improbable just a few years ago. In 2025, AI models (particularly large language models and agentic systems) are not only powering digital assistants and customer support, but are also making

Retrieval-Augmented Generation (RAG) has rapidly emerged as a cornerstone of modern AI applications, empowering systems to deliver contextually rich and accurate responses by marrying powerful language models with external, authoritative knowledge bases. Yet, as organizations increasingly deploy RAG-powered solutions for search, customer support, recommendation engines, and more, the complexity of

Introduction The rise of Large Language Models (LLMs) has transformed the landscape of AI-driven applications, from conversational agents to advanced search systems. As these models permeate enterprise workflows, the challenge shifts from building impressive prototypes to maintaining reliable, scalable, and high-quality production systems. LLM observability (the practice of monitoring, tracing,