Arize AI

Announcing coding harness tracing for observing, evaluating, and improving coding agent workflows across Claude Code, Cursor, Codex, GitHub Copilot, and Gemini CLI. The post Coding agent tracing and evaluation: An open source tool to improve AI coding workflows appeared first on Arize AI .

Material Event filed 2026-05-15

How Arize uses Alyx to debug Alyx: searching dense traces, aggregating failures, triaging dogfooding issues, and closing the AI engineering feedback loop. The post How we use Alyx to build Alyx: How to build an AI agent feedback loop appeared first on Arize AI .

A year ago, frontier models started losing track of instructions somewhere around 200–300 simultaneous constraints. With 2026 models, that ceiling is closer to 2,000 — an order-of-magnitude jump. We re-ran IFScale to see how, and how each model fails. The post Models got an order of magnitude better at following instructions in one year appeared first on Arize AI .

As agents start changing software, they need a way to verify their work that includes traces, evals, feedback, and APIs. This is where Phoenix goes next — not the next release, but what this product becomes. The post From observability to context: What’s next for Arize Phoenix appeared first on Arize AI .

A benchmark-driven look at why agent harnesses need adaptive finish logic as model behavior changes across Claude, GPT-4o, and Gemma. The post Agent harnesses have an expiration date appeared first on Arize AI .

Lessons from building and shipping Alyx, our AI agent The post AI agent evaluation: How to test, debug, and improve agents in production appeared first on Arize AI .

As we have built our own harness management tools internally at Arize, and watched external systems like Devin @cognition start managing other Devins, managed agents at @AnthropicAI and long running The post Swarm management in agent harnesses: owning long-running agents appeared first on Arize AI .

An evaluation harness is the standardized infrastructure that decides what gets evaluated, runs the evaluation, and acts on the result. The post What is an evaluation harness? appeared first on Arize AI .

Twitter said pick a side. The eval said the question was wrong. Six months ago, MCP (model context protocol) was the hot new thing: tool usage with a built-in discovery... The post MCP vs. CLI Skills for agents: what our eval found (and which you should use) appeared first on Arize AI .

Enterprise agents are moving from demos into production workflows, which creates a basic problem: teams need to understand what those agents actually did. The post Why agent telemetry needs standards appeared first on Arize AI .