AI monitoring is production observability for model-driven systems: traces, cost, latency, tool failures and output-quality drift.
1 min readupdated 2026-08-01
/ quick answer
Standard APM misses what breaks AI systems. You need per-request traces with the full prompt and tool calls, cost and token counts, tool error rates, refusal and fallback rates, escalation rate, and sampled quality scoring. Silent degradation — output that is worse but still valid — is only visible if you sample and score continuously.
AI monitoring is production observability for model-driven systems: traces, cost, latency, tool failures and output-quality drift. Standard APM misses what breaks AI systems. You need per-request traces with the full prompt and tool calls, cost and token counts, tool error rates, refusal and fallback rates, escalation rate, and sampled quality scoring. Silent degradation — output that is worse but still valid — is only visible if you sample and score continuously. In practice: An alert fires when escalation rate crosses 25% or when average tool retries per run doubles week-on-week. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
Standard APM misses what breaks AI systems. You need per-request traces with the full prompt and tool calls, cost and token counts, tool error rates, refusal and fallback rates, escalation rate, and sampled quality scoring. Silent degradation — output that is worse but still valid — is only visible if you sample and score continuously.
Example
An alert fires when escalation rate crosses 25% or when average tool retries per run doubles week-on-week.
Standard APM misses what breaks AI systems. You need per-request traces with the full prompt and tool calls, cost and token counts, tool error rates, refusal and fallback rates, escalation rate, and sampled quality scoring. Silent degradation — output that is worse but still valid — is only visible if you sample and score continuously.
What is an example of AI Monitoring?
An alert fires when escalation rate crosses 25% or when average tool retries per run doubles week-on-week.
Why does AI Monitoring matter for AI and automation?
AI monitoring is production observability for model-driven systems: traces, cost, latency, tool failures and output-quality drift. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.