> ## Documentation Index
> Fetch the complete documentation index at: https://agent-observability-docs.splunk.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Core Observability concepts in Splunk Agent Observability

## 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, Splunk Agent Observability captures every session, trace, and span, producing a structured stream of real-time data.

* [Agent Streams](/sdk-api/logging/logging-basics) and [projects](/concepts/projects) organize the data you send to Splunk Agent Observability for a given application or environment.
* [Sessions](/concepts/logging/sessions/sessions-overview) group related traces into a complete multi-turn interaction.
* [Traces](/sdk-api/logging/splunk-ao-logger#start-a-trace) represent a single turn, request or AI workflow.
* [Spans](/sdk-api/logging/splunk-ao-logger#add-spans) capture the individual steps within a trace, such as LLM calls, tool calls, or a retrieval step.

## Getting started

Start with [Instrumentation](/sdk-api/logging/logging-basics) to understand how data is structured in Splunk Agent Observability and how to send logs from your application.