AI agents feel magical when they work. But without the right metrics, they quickly become expensive, unreliable black boxes that erode user trust and drain budgets.
This is your battle-tested playbook for monitoring the 20 metrics that separate impressive demos from production-ready systems that scale. Think of these as five critical dimensions every team must master: Token Economics, Response Quality, Agent Behavior, System Health, and Business Impact.
Because metrics without action are useless, I'll show you how to implement progressive alerting, create executive dashboards, and build the operational muscle to catch problems before they become disasters.
đź’ˇ New to AI PM observability? This guide builds on the foundational concepts from my Guide to AI PM Observability. Start there if you want the complete picture of why observability transforms how you ship AI products.
Let's dive into the metrics that matter:
Cost is the first failure mode most teams hit. Tokens feel cheap during development—fractions of a cent per call—but at scale, invoices compound exponentially. You need visibility at micro (per-model), macro (per-user), and strategic (per-feature) levels.
1. Token Usage by Model
2. Cost per Conversation/Session
3. Cache Hit Rate