LLM Observability, Evaluation, Experimentation Platform — Dat Ngo, Arize

Your agent called tool B before tool A, and B has a dependency on A. You did not catch it because nothing in your code audits agents. The telemetry does. Dat from Arize AI walks through what observability actually means when the system you are debugging is nondeterministic and the execution path changes with every run. The talk covers the five flavors of eval signal (LLM as judge, human feedback, golden datasets, deterministic checks, business metrics), what scope to run them at (single span, multispan, trajectory, session), and where this is heading. Arize Phoenix is open source, runs as a single container, no Kubernetes required. The enterprise product adds an AI layer called Alex that scans traces, surfaces high latency and errors, and creates evals automatically. The stated goal: automate you out of the observability loop entirely. Speaker info: - https://www.linkedin.com/in/datdarylngo/ - https://x.com/dat_attacked