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8 Datadog AI Observability Concepts Every Beginner Should Understand
Here are 8 essential concepts to understand when getting started with Datadog Agent Observability.
1. Why AI Applications Need Observability
When something goes wrong in a traditional application, developers often follow the logs to identify the problem.
AI applications however, introduce additional complexity because LLM outputs and agent execution paths are not always predictable.
Datadog Agent Observability provides end-to-end visibility into this execution process.
It helps you:
- Understand how a request moves through your application.
- Identify failed or slow operations.
- Inspect LLM inputs and outputs.
- Track tool calls and their results.
- Investigate token usage and performance.
2. Learn How Traces Represent an Entire Request
A trace represents the complete execution path of a single request.
It groups together the operations involved in processing that request, allowing you to follow the execution from beginning to end.
