Blog — page 6.
Evals in CI: How to write your LLM evals as tests with Arize Phoenix
If you're struggling to get started with evals, you're not alone. This post explains how to write LLM…
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Own the loop: A field guide to agent harnesses
As models become cheaper and more interchangeable, the durable advantage shifts to the agent harness: the loop, tools,…
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Model subsidies are ending. What do you do now?
Flat-rate AI plans are subsidizing agentic workloads. Learn why LLM inference costs are moving to metered pricing and…
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AI evals are a data science problem: What most teams get wrong
Hamel Husain explains why the best AI teams treat LLM judges like classifiers, not dashboards.
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Why AI token costs don’t tell you if your AI is working
Token spend does not prove AI is creating value. Teams need cost-per-outcome metrics that connect AI usage to…
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Meet PXI: the AI engineering agent inside Phoenix
An AI engineering agent built into Phoenix. It works like a coding agent, just point it at your…
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Agent harness vs. agent framework: why harnesses are replacing frameworks
Agent harnesses are replacing frameworks as the real product surface for reliable AI agents, shifting the work from…
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One agent, two trace destinations: Arize AX + Databricks Unity Catalog
Send one OpenTelemetry trace stream to both Arize AX and Databricks Unity Catalog so engineers can debug agents…
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Bring production agent traces from Arize into Databricks Unity Catalog
Arize Data Fabric now supports Databricks, helping teams sync production agent traces, evaluations, and annotations into customer-owned storage…
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How Arize built AI-native support workflows that cut resolution time in half
Arize reduced median support resolution time from 22 hours to roughly 2.5 hours by building AI-native internal workflows…
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Phoenix at 10,000 stars on GitHub: How an open source AI observability project grew by following its community
Phoenix crossed 10,000 GitHub stars. Here is how the open-source AI observability project grew from a Jupyter notebook…
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Building the AI factory for self-improving agents: What’s new in Arize AX
Arize AX is adding managed agents, full-agent experimentation, expanded multimodal support, and Harness-as-a-Judge to help teams observe, evaluate,…
Read the postDon’t ship vibes.
Arize gives AI teams observability and evals to understand and improve agent performance.
