Fact Sheets
See Inside Your AI: The AI Observability Overview
Learn how GoodData.AI tracks adoption, quality, and cost across your agents, traces any answer back to the step where it failed, and recommends the fix.
Description
As organizations move AI agents into production, many can't see what happens inside them. When an answer is slow, wrong, or expensive, it's hard to tell which skill, piece of knowledge, or memory caused it, or whether the agent is worth scaling.
GoodData.AI Observability opens up every answer. It tracks adoption, quality, tokens, and cost across your agents, and lets you trace any response step by step: which skills ran, what knowledge and memory were pulled, and where it failed. Patterns across conversations come with recommended fixes to knowledge, semantic models, or configuration.
Inside, you'll learn:
- How it works in three steps: track usage, trace an answer, and fix what repeats.
- What a single trace reveals, with a worked example showing timing, tokens, missing context, and errors per step.
- Which questions your teams can answer, from AI engineers and data teams to product and compliance.
- How it fits the stack you already run: a workspace in your own org, no separate data pipeline, and prebuilt dashboards you can extend.
- Where it runs: GoodData Cloud, or GoodData.CN on your infrastructure.
Get the one-page overview, then book a live demo to see usage, tracing, and fixes in action.
Does GoodData look like the better fit?
Get a demo now and see for yourself. It’s commitment-free.
