The 6 layers of AI observability: From infrastructure to agents

Observability is crucial in software engineering to understand internal states and ensure reliable performance, particularly in AI systems where non-deterministic outputs pose unique challenges. Unlike traditional software, AI applications like large language models can produce variable results, making observability essential for tracking outcomes, reasoning processes, and variations. This is important for building trust and moving AI systems from experimental to operational stages by providing audit trails and comprehensive visibility across six interconnected layers: infrastructure, data retrieval, model interaction, agent reasoning, workflow orchestration, and user application. Each layer serves a specific function, from monitoring resource usage and retrieval quality to capturing model interactions and agent decisions, ultimately impacting user experiences and feedback. Observability tools, such as those offered by platforms like Retool, enable systematic evaluation and optimization by recording detailed logs, analyzing decision patterns, and integrating user feedback to improve AI reliability and performance in production environments.