Observability

Large language models are rapidly becoming central to enterprise operations. ChatGPT alone had over 400 million weekly active users as of February 2025, while AI-powered features now appear across countless production applications. This widespread adoption has made AI monitoring a critical requirement for organizations deploying LLM-powered systems at scale. AI

The rapid adoption of AI agents in enterprise environments has created a critical need for robust observability frameworks. According to PwC, eight in ten enterprises now use some form of agent-based AI, yet many organizations struggle with the complexity of monitoring these autonomous systems. As AI agents become more sophisticated,

TLDR LLM monitoring is the discipline of observing AI systems end-to-end covering prompts, parameters, tool calls, retrievals, outputs, cost, and latency to overcome the limits of traditional Application Programming Monitoring (APM) in non-deterministic, multi-step agent workflows. The guide outlines core risks (hallucination, prompt injection, response variance, and cost-performance tradeoffs) and

Enterprise AI observability has become an operational necessity as organizations deploy AI systems at scale, requiring specialized techniques to track, analyze, and optimize the complex behavior of machine learning models and AI agents. What is AI observability AI observability is a shift in monitoring technology systems, providing visibility into AI