Observability Platforms

Observability platforms capture, store, and visualize the full execution telemetry of agent systems — traces, token usage, latency, cost, tool calls, and reasoning chains — giving you the production monitoring infrastructure that makes agents debuggable at scale. Observability comes before optimization in this sequence for a concrete reason: without a trace showing you which step consumed 40 of a 45-second run, any optimization attempt is a guess, and you will just as likely make things slower. Unlike traditional application performance monitoring tools built for deterministic software, large language model observability platforms handle non-deterministic systems where "errors" are often subtle reasoning failures rather than thrown exceptions, and tools like LangSmith and Arize Phoenix are built specifically to surface those failures.