How to build observability for the agentic AI era

As agencies move AI agents from experimentation into production, traditional infrastructure monitoring can leave critical blind spots. Agentic systems introduce new challenges around unpredictable outputs, security risks, infrastructure performance and rapidly growing compute and token costs.

This guide from Splunk explores how organizations can build a more complete approach to AI observability that connects agent behavior, infrastructure health, security guardrails and cost controls. Learn how teams can evaluate AI outputs continuously, trace performance across the full technology stack and identify issues before they undermine trust or derail AI investments.

Download the guide to learn more.