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Arize Phoenix enables local-first, air-gapped observability for coding agents, allowing each agent to have its own traces, evals, and feedback loop for self-verification.
Arize Phoenix announces a free 2-hour evaluations workshop from the AI Engineer: Europe conference, led by head of DevRel Laurie Voss, covering manual data examination and built-in/custom evals.
The official TanStack AI OpenTelemetry support is now available, offering an open-source backend for traces, datasets, and replay to improve debuggability.
This article discusses best practices for LLM application development using Arize Phoenix, specifically highlighting the importance of using train/validation/test splits for honest evaluation and tracking regressions.