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All digests tagged Arize AI
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Arize's agent Alex evolved from a 50% hallucination prototype to a robust system. Key lessons include moving planning into custom harness code, treating large tool results as files, and using small markdown files for long-term memory.
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Fuad Ali details the self-improvement loop for AI agents: trace failures, use Signal to detect issues, create living regression datasets, and run experiments to prove fixes. This automates reliability.
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The AI Engineer role is shifting from writing features to building self-improving loops. This requires building always-on agents that consume telemetry, run in durable sandboxes, and are continuously tested using advanced evaluation systems.
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AI agents have made writing code nearly free, shifting the bottleneck from coding to defining the problem. The future of engineering lies in the Product Engineer role: figuring out what 'good' means.
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To ensure AI agent reliability, the speaker details a five-step regression loop. This process uses production traces as the source of truth, employing LLMs as judges to quantify subjective metrics and integrating automated gates into CI/CD.
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