What is AI Observability
Agentic applications are inherently non-deterministic, meaning their behavior cannot be fully predicted or exhaustively tested before deployment. As a result, traditional monitoring approaches fall short in capturing how these systems behave in production. AI observability provides visibility into the unique runtime behavior of AI applications, allowing teams to understand what is happening under the hood, why it is happening, and how it impacts performance and outcomes.Core concepts
Once instrumented, Galileo captures every session, trace, and span, producing a structured stream of real-time data.- Log streams and projects organize the data you send to Galileo for a given application or environment.
- Sessions group related traces into a complete multi-turn interaction.
- Traces represent a single turn, request or AI workflow.
- Spans capture the individual steps within a trace, such as LLM calls, tool calls, or a retrieval step.