AI agents are moving from demos to production, but observability hasn't kept up. When an agent takes a wrong path, hallucinates mid-task, or silently degrades, how do you investigate? Traditional APM treats agent execution as a black box. We need purpose-built, OpenTelemetry-native observability for agentic AI.

We introduce the Agent Traces and Agent Health for OpenSearch: a native UI for exploring agent execution traces. OTel SDKs with GenAI semantic conventions (gen_ai.* attributes) instrument your agents, Data Prepper ingests the spans, and Agent Traces show you hierarchical trace views, detail agent maps, and aggregate metrics like token usage and latency percentiles - all queryable via PPL.

We demonstrate root-cause investigation: expanding execution trees to inspect each LLM call and tool invocation, querying spans to answer "which tool call caused the agent to diverge?" We then go deeper with Agent Health's golden path comparison that evaluates trajectories against expected behavior. Whether you're building agents for customer support, code generation, or data pipelines, you'll leave with a practical playbook for agent observability.