😶‍🌫️ Blind Spots & Broken Bots: AI Agent Observability

**Blind Spots & Broken Bots** Getting an AI agent running is one thing. Understanding what it's doing when something goes wrong is another. As agents become more complex, debugging failures gets harder. A failed tool call, an unexpected LLM response, or an agent stuck in a loop might not trigger a traditional alert. Without visibility into what's happening inside the workflow, troubleshooting quickly turns into guesswork. For this **Prompt Engineers AI** session, we'll explore how to bring observability to AI agents using an open-source, vendor-neutral stack built around **LangChain, OpenTelemetry, and the OpenTelemetry Collector.** We'll look at how to instrument agents, collect telemetry, and route traces, metrics, and logs into **Prometheus, Tempo, Loki, and Grafana**. We'll also cover how using the OpenTelemetry Collector helps keep observability backends separate from application code, whether you're running agents in Docker, Kubernetes, or another containerized environment. **What we'll cover:** * **Agent tracing:** Follow agent execution from LLM interactions through tool calls to better understand behavior and failures. * **Telemetry collection:** Use OpenTelemetry and the Collector to route traces, metrics, and logs. * **Visualizing agent behavior:** Build Grafana views to help identify failures and troubleshoot workflows. * **Vendor-neutral observability:** Understand how to change observability backends at the Collector without modifying agent instrumentation.