Observability

Agent observability is the practice of monitoring, analyzing, and understanding the internal state and behavior of AI agents in real time. It is a technical discipline focused on making the actions, decisions, and internal processes of AI agents transparent and measurable. In the context of LLM-powered agents or multi-agent systems,

TL;DR: 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

Introduction 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 helping make decisions in healthcare,

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 organizational knowledge bases. Yet, as organizations increasingly deploy RAG-powered solutions for search, customer support, recommendation engines, and more, the complexity of these

TL;DR: LLM observability is the foundation for debugging and improving large language models in production.  It combines tracing, evaluation, and monitoring to capture every prompt, generation, and feedback loop across workflows. This guide explains key concepts like traces, spans, and retrievals, and shows how Maxim AI implements full-stack observability