Best Practices for Production AI Agents: Observability and Tracing (www.arthur.ai)

A new multi-part series on building reliable AI agents has begun, focusing on critical aspects of observability and tracing essential for production readiness. The initial installment emphasizes the significance of these capabilities by sharing insights from the Forward Deployed Engineering (FDE) team’s experiences across various industries. Key examples highlight how leveraging observability tools like tracing can significantly bolster an agent’s trustworthiness, helping teams identify problem areas and prioritize their improvements effectively—ultimately enabling successful deployments rather than getting stuck in demo phases. The article further discusses the use of OpenTelemetry as a standard for achieving vendor-neutral observability, enabling teams to trace agent behavior seamlessly across systems. It also introduces the OpenInference semantic conventions, which provide richer details for production workloads compared to competing standards. The benefits of implementing observability at foundational areas, including LLM calls, tool invocations, and RAG calls, are underscored as critical for efficient debugging and performance analysis. The post concludes by recommending frameworks with robust observability support to streamline the development process, ensuring that AI agents can move confidently into production environments.