Short answer: use pre-aggregated metrics for the charts in a gaming AI-agent admin dashboard, keep structured logs for investigation, and make rollback safety a release requirement for both paths. A metrics API is usually the simpler backend for latency, cost, throughput, and error KPIs; logs are the evidence you need when a single agent turn has to be reconstructed.
That recommendation is intentionally boring. A live operations page should not rediscover a timeseries by scanning every event each time a Node.js admin user opens it. The page needs a bounded query, a freshness target, and a number that can be compared with an SLO. The incident queue needs the original events, correlation identifiers, and enough context to explain why one game session took an unexpected route.
Rollback is the decision axis here. If a new prompt, tool policy, or model route increases latency or cost, the team must be able to switch it off without losing the measurements that explain the decision.
EU GDPR data boundaries for metrics and logs
Start with the question behind each chart. “What was the p95 agent latency by release over the last hour?” is a metric question. “Why did session 8f2a call the inventory tool three times?” is a log question. “Did the rollback reduce spend without increasing failed tool calls?” needs both, joined by a release label and a correlation identifier that is not itself a personal identifier.
