Observability (AI Systems)
LLM observability is the systematic monitoring, tracing, and analysis of AI systems and language models in production. Unlike traditional software observability (logs, metrics, traces), LLM observability addresses the specific challenges of generative AI: non-deterministic behavior, complex prompt chains, tool calls, and cost-per-request dynamics. The core components include: LLM tracing (end-to-end tracking of prompts, responses, and metadata per request including tokens, latency, and model used), tool monitoring (in agentic systems like Model Context Protocol, every tool call is logged with its input and output), cost tracking (token consumption and API costs aggregated per request, user, or feature), quality evaluation (automated or manual assessment of response quality, hallucination rate, and prompt adherence), and alerting (thresholds on latency, error rate, or cost spikes trigger notifications). Tools like Langfuse (built in Berlin) and Honeycomb have become production standards for LLM observability. Without observability, it is impossible to identify quality issues, security incidents like prompt injection attacks, or cost drivers in AI systems — making it non-negotiable for any production-grade AI deployment.