Strands Agents, an open-source Python framework developed by AWS, simplifies the creation of production-ready AI agents by abstracting orchestration and allowing models to plan and execute tasks autonomously. However, these applications pose challenges in maintaining visibility and predictability of multi-agent workflows, often resulting in performance issues and debugging complexities. Datadog LLM Observability addresses these challenges by providing out-of-the-box visibility for Strands Agents, enabling developers to trace, measure, and evaluate agent workflows without custom instrumentation. It captures key operations within the agent lifecycle, offering end-to-end traceability and correlating model performance with infrastructure health. Developers can debug workflows, evaluate safety and quality, and experiment with models using real production data, identifying inefficiencies and optimizing multi-agent processes. Additionally, Datadog's evaluation tools help detect errors such as hallucinations and unsafe responses, allowing for rapid diagnosis and improvement of AI applications. By integrating seamlessly with Datadog's monitoring products, this observability tool enhances the reliability and performance of AI agent deployments.