These two things sound similar but solve completely different problems. In this clip from our Superweek session, Jon Su breaks down the distinction:
- AI Agent Observability (think LangFuse, LangChain): looks at the internal workings of an agent. Prompts, responses, token usage, latency. Built for ML engineers to improve model performance.
- AI Agent Analytics: looks at intent. Why is a user interacting with your agent? What are they trying to achieve? What does that mean for your business?
The analogy: server-side logs were fine until we needed more granularity, and that's what sparked the explosion of modern web analytics. AI agent observability is the server log. AI agent analytics is what comes next.
🎥 Watch the full session: https://snowplow.io/events/track-ai-agents-in-2026