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As AI agents move deeper into production workflows, observability has shifted from a nice-to-have to a core engineering requirement. Without visibility into how your agents reason, make decisions, and fail, debugging production issues becomes guesswork and improving quality becomes nearly impossible. This guide covers five platforms that give

As AI agents move from prototypes to production, ensuring they behave reliably across complex, multi-step workflows has become a critical engineering challenge. Agent observability is no longer optional it is the infrastructure layer that tells you when your agent failed, why it failed, and what to do about it.

Retrieval-Augmented Generation (RAG) pipelines introduce multiple failure points poor retrieval quality, context truncation, hallucinated synthesis that standard LLM monitoring tools aren't built to catch. RAG observability addresses this by giving teams visibility into every stage of the pipeline: retrieval, context assembly, and generation. This guide covers five