Observability and LLM-Native Metrics
Explore how to design and interpret observability metrics for large language model agents, including tracing request execution, identifying latency percentiles, auditing authorization, and detecting quality drift. This lesson helps you understand the evidence needed to explain system behavior, costs, and performance for operational and interview scenarios.
At release review, the dashboard shows 98 HTTP successes from 100 requests, but the review finds only 88 supported answers. Explaining that gap is an interview skill: Specify evidence that separates service availability, execution correctness, answer quality, and cost.
Consider a maintenance assistant that answers inspection-interval questions using approved documents and a read-only equipment-status tool. Request R095 answers “90 days,” although the current manual says “60 days.” A final-response log cannot explain where the discrepancy entered the workflow.
Build an observability contract covering agent steps and tool calls, token speed, latency percentiles, complete request cost, audit evidence, and quality-drift alerts.
Explaining R095
A trace represents one request’s execution through correlated operations. A span records an operation within that trace, with its timing, parent relationship, outcome, and identity. Together, these records let an investigator follow a request ...