AI agent analytics, explained.
AI agent analytics is the practice of analyzing every AI agent conversation as the unit of analysis — detecting user intents and journeys, auto-classifying issues by their impact on agent health, and verifying that the fix actually worked. It is distinct from product analytics, which measures events, and LLM observability, which traces the runtime.
What agent analytics measures
Traditional analytics stop at clicks and pageviews. Agent analytics reads the conversation itself — what the user actually wanted, what the agent did about it, and whether the outcome was good. The core objects are intents (what users ask for), journeys (the paths they take), and issues (where the agent fails), rolled into a single Agent Health Score.
Why product analytics and observability fall short
Product analytics suites read events — they were built for funnels and retention, not conversation content, and are only now bolting on agent features. LLM observability traces spans and latency for engineers debugging the system. Neither was built to read meaning, score an agent's health, or prove a shipped fix worked — the jobs agent analytics exists to do.
What to measure
- Intents — Semantic clusters of what users actually ask your agent for, with emerging trends.
- Journeys — The aggregated paths users take, and whether they complete with satisfaction.
- Issues — Auto-detected failures, classified and scored by their impact on agent health.
- Agent Health Score — A single composite metric you can track over time.
- Closed loop — Verification that a shipped fix actually moved the metric.
How it works
Brizz captures sessions from your code with an OpenTelemetry-compatible SDK — two lines and every conversation is analyzed. See how Brizz works step by step, or the integration guide for fitting it alongside the observability you already run.
Agent analytics vs the alternatives
See how dedicated agent analytics compares to the tools teams already use:
Agent analytics, answered
AI agent analytics is the practice of analyzing every AI agent conversation as the unit of analysis — detecting user intents and journeys, auto-classifying issues by their impact on agent health, and verifying that fixes actually worked.
LLM observability (LangSmith, Langfuse) traces what the system did — spans, prompts, latency, errors — for engineers. Agent analytics reads what users wanted and whether the agent delivered, for the whole team.
Product analytics (Mixpanel, Amplitude) measures events and clicks. Agent analytics reads the conversation itself — the intents, decisions, and outcomes that no event ever captures.
A single composite score that summarizes how well your agent is performing across intents, journeys, and issues — so you can track whether changes actually moved the needle.
Yes — they're complementary. Keep your tracing and product-analytics stack, and add agent analytics for the conversation layer they weren't built to read.
Put agent analytics on your agents.
Two lines of code and every conversation is analyzed.