LLM Evaluation & Monitoring

The most expensive failure in enterprise AI isn't a single hallucination. It's unmeasured degradation. Agents can look good in demos and early pilots, then quietly drift as documents change, tools evolve, and prompts accumulate patches. Without evaluation and monitoring, you discover failure through user frustration rather than metrics.

Evaluation defines whether the agent behaves correctly. Monitoring detects whether it stays correct in production. Both require you to define quality explicitly. There are behavioral metrics (does the agent follow policy, remain grounded, and use tools correctly?) and business metrics (does it reduce cycle time, increase deflection, or improve pipeline quality?). The trap is optimizing for "sounds good" while outcomes don't improve.

Offline evaluation commonly uses scenario-based test suites, golden datasets, and regression coverage across critical behaviors. Retrieval systems require special focus: groundedness checks, citation relevance, and refusal behavior when sources are missing or permissions disallow access. Online monitoring adds tracing, structured logs, tool-call analytics, and alerting for spikes in escalations or unsafe patterns.