Everything we’ve published — page 9.
How to build LLM-as-a-Judge evaluators that hold up in production
Learn how to design, calibrate, and run LLM-as-a-judge evaluators with fixed labels, human agreement checks, trace context, and…
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What we learned testing 7 models under the same agent harness
Model swaps look like configuration changes, but they behave more like product migrations. A new model may be…
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Braintrust alternatives for AI observability & agent evaluations
Compare the top Braintrust alternatives for AI observability and evaluation, focusing on tools that support tracing, debugging, and…
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Building a self-improving agent on a context graph of human disagreement
You can build a measurably better agent from data you already have, without retraining a thing. The data…
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Coding agent tracing and evaluation: An open source tool to improve AI coding workflows
Announcing coding harness tracing for observing, evaluating, and improving coding agent workflows across Claude Code, Cursor, Codex, GitHub…
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How we use Alyx to build Alyx: How to build an AI agent feedback loop
How Arize uses Alyx to debug Alyx: searching dense traces, aggregating failures, triaging dogfooding issues, and closing the…
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AI agent analytics platforms: A buyer’s guide
What to measure, how to evaluate, and what to require when buying analytics for production AI agents—spans, traces,…
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Models got an order of magnitude better at following instructions in one year
A year ago, frontier models started losing track of instructions somewhere around 200–300 simultaneous constraints. With 2026 models,…
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From observability to context: What’s next for Arize Phoenix
As agents start changing software, they need a way to verify their work that includes traces, evals, feedback,…
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Agent harnesses have an expiration date
A benchmark-driven look at why agent harnesses need adaptive finish logic as model behavior changes across Claude, GPT-4o,…
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AI agent evaluation: How to test, debug, and improve agents in production
Lessons from building and shipping Alyx, our AI agent
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Swarm management in agent harnesses: owning long-running agents
As we have built our own harness management tools internally at Arize, and watched external systems like Devin…
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Arize gives AI teams observability and evals to understand and improve agent performance.
