What Is AI Agent Observability? A Practitioner Guide
Splunk, Thursday, October 22nd, 2026
Splunk explains AI agent observability, built on traces, evals, cost and guardrail decisions to judge whether an agent was right.
Splunk says AI agent observability answers whether an agent was right, not just whether it ran.
An agent can pick the wrong tool or loop on the same search while every span reports success within its latency budget.
The practice rests on four signals: traces, evaluations, cost and guardrail decisions.
Splunk notes sampling is a common blind spot and says small purpose-built evaluator models can cost far less than LLM-as-judge, making it affordable to grade every interaction.