Arize AI published a guide on building LLM-as-a-Judge evaluators that function reliably in production, emphasizing that code-based checks should handle deterministic tasks like schema validation while…
Seven large language models tested under the same agent harness showed similar correctness scores but significant differences in operational behavior, including latency, tool-call counts, and timeout …
A team built a procurement agent that improved its decision-making accuracy from 53.8% to 83.1% by capturing human reviewer overrides as a context graph, without any code changes or model retraining. …
Arize AI today launched coding-harness-tracing, an open source tool that captures step-by-step traces of coding agent workflows across Claude Code, Cursor, Codex, GitHub Copilot, and Gemini CLI. The t…
Alyx, an AI agent designed to help teams improve their own AI agents, is being used by its developers at Cursor to build and debug itself. The team built the Alyx Trace Debugger to search across dense…
New research shows frontier AI models have improved nearly tenfold in their ability to follow instructions over the past year, according to data from the IFScale benchmark. A year ago, models began lo…
Arize Phoenix is evolving from an observability tool designed for human-in-the-loop workflows into a context platform for both humans and AI agents. The shift comes as coding agents increasingly write…