What is AI Observability? A Complete Guide to Debugging and Monitoring Modern AI Systems at Scale
Blog post from Comet
AI observability extends traditional monitoring by capturing AI-specific telemetry such as prompts, outputs, agent decisions, retrieval results, tool calls, evaluations, and token costs, addressing the behavioral and semantic failures that infrastructure dashboards cannot detect. Because LLMs, RAG pipelines, and agents are non-deterministic and may return incorrect, unsafe, ungrounded, or unnecessarily costly responses even when latency and error metrics appear normal, effective observability must connect application outcomes, orchestration paths, model behavior, and retrieval quality in a unified trace. Its core signals include structured logs for searchable execution data, metrics for quality, safety, and cost trends, traces for reconstructing each request’s full path, and evaluations using rules, LLM judges, and human feedback to assess whether outputs were successful. The recommended implementation approach is to instrument one important workflow first, add trace-level telemetry and a small set of quality evaluations, associate costs with outcomes, and then standardize these practices across AI features. The passage presents Opik, an open-source platform from Comet, as a tool intended to provide tracing, evaluation management, and cost monitoring for LLM applications, RAG systems, and agents.
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