Practical writing on AI agent analytics, AI product analytics, and AI observability — for teams shipping AI-native products and measuring what actually matters.
How to trace, measure, and improve AI agents in production — from tool call analytics to agent performance and retention.
Agentic AI products face unique retention challenges that traditional product strategies miss. Here is what drives churn in AI-native apps and the measurement frameworks that fix it.
A direct comparison of Trodo and LangSmith — what each does, who it serves, and how to decide which one (or both) your AI team needs in 2026.
Agent analytics, agent observability, and LLM observability get used interchangeably — but they are not the same. Here is a clear disambiguation of each term, the audiences they serve, and how they fit together in a production AI stack.
Everything product teams need to know about AI product analytics in 2026 — what it measures, how it differs from traditional analytics, and how to build a measurement foundation for AI-native applications.
AI agent observability and AI agent analytics are often confused but serve different audiences and answer different questions. Here is how to think about both and when you need each.
A practical guide to the KPIs, metrics, and measurement approaches that product teams use to evaluate AI agent performance in production — from trace-level data to business outcomes.
A practical comparison of the leading AI agent analytics tools in 2026 — covering what each does, who it is for, and how to choose the right stack for your product team.
AI agent analytics explained: how to trace LLM agents, measure tool call success, connect agent performance to product outcomes, and why flat event tracking is no longer enough.
Why AI product analytics blends traditional product metrics with model and agent signals—and how to build a coherent measurement stack for AI-native products.
A practical guide to agent analytics: tracing orchestrations, tool calls, latency, and failure modes so you can ship reliable AI features and prove value with data.
Measuring AI-native product behavior — feature adoption, chatbot funnels, retention, and a unified view of AI + classic product data.
Shipping AI features changes what you need to measure, how you measure it, and how you act on the data. Here is how to evolve your product analytics practice for AI-powered products.
How to measure, improve, and grow products built around chatbots and AI copilots — the product analytics approach that goes beyond session counts to trace-level behavioral insight.
Measuring AI feature adoption requires different metrics than traditional feature tracking. This guide covers the frameworks, signals, and analytics approaches that work for AI-powered products.
Traditional product analytics was built for apps with clicks and screens. AI product analytics is built for apps with prompts, tool calls, and agents. Here is what changes — events, funnels, retention, and the questions each can answer.
The engineering side: LLM observability, AI observability tools, and how observability complements analytics for AI systems.
LLM observability and product analytics answer different questions for different teams, but they share one goal: making AI products better. Here is how to use both effectively.
A practical playbook for agent observability: what to instrument, which signals matter, how to connect agent traces to product KPIs, and the mistakes most teams make in their first six months.
AI observability explained: what it is, how it differs from traditional observability and LLM observability, the signals that matter for AI agents and LLM-powered products, and how it fits alongside AI product analytics.
A practical 2026 comparison of AI observability tools — Arize, Langfuse, Helicone, Datadog LLM, Honeycomb, and Trodo — covering what each does well, where they fall short, and how to choose the right one for your AI product.
Background on product analytics fundamentals and head-to-head comparisons of popular analytics tools.
A direct comparison of Mixpanel, Amplitude, and Trodo for product analytics in 2026 — covering strengths, weaknesses, and which platform fits which team, especially those shipping AI features.
A practical guide to product analytics for SaaS companies — covering the core frameworks, key metrics, and modern approaches for teams building products that include AI features.
Learn what product analytics means in practice: events, funnels, cohorts, and how teams use them to improve activation and retention—with a clear lens on modern product intelligence.
A practical comparison of the leading AI product analytics tools in 2026. We cover what each platform does well, what it misses, and how to pick the right stack for AI-native product teams.
Trodo and PostHog both call themselves product analytics platforms, but they target different problems. Here is a clear comparison for teams shipping AI-native products in 2026.
