Observability

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,

TL;DR Retrieval-Augmented Generation (RAG) combines retrieval and generation to produce accurate, context-rich responses, but debugging these systems can be complex. Common issues include irrelevant retrievals, hallucinations, context overflow, and latency. Effective debugging requires evaluating retrieval quality (precision, recall, MRR, MAP) and generation accuracy (faithfulness, relevance, correctness) while tracing and

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

TL;DR Multi-agent systems introduce debugging complexity due to emergent behavior, cascading failures, and non-deterministic tool calls. Agent tracing solves this by capturing every decision, message, and state transition across all agents in a workflow. Platforms like Maxim AI provide distributed tracing, visual replay, automated evaluation, and in-context debugging for