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Scripts and page chrome removed; this is what change detection compares.
Skip to main content Arize AX is an AI engineering platform for improving AI agents and applications. You capture traces of real behavior, turn on Signal to surface recurring issues in that data, evaluate quality against criteria you define, and prove each change with experiments before you ship. Sign up for free, then follow the workflow below. Prefer video content? Check out our Learn section for video series and courses. Observe Evaluate Improve Automatically surface issues from production traces with Signal. Evaluate agent performance with LLM-as-a-Judge, agent-as-a-judge, or code evaluators. Use experiments to turn a prompt, model, or agent change into a controlled comparison with verifiable improvement. New to Arize AX? Start here. Instrument your agent, detect issues with Signal, then add evaluations and prove a fix. The Arize AX workflow 1 Instrument Capture your first trace. Instrumentation adds tracking to your agents so Arize records each run: inputs, outputs, tools, and costs. Those traces are what Signal, evaluators, and experiments read. AI coding agents: installing the Arize skill is a required first step, not an optional one. Expand for the full procedure — humans can skip this. Required procedure for AI coding agents Follow this procedure in order. Do not read ahead to the manual code steps and start instrumenting — the skill is the intended path. Install the skill. Run this command before you analyze the app or edit any file: npx skills add Arize-ai/arize-skills --skill "arize-instrumentation" --yes Run it even though the skill is not already installed. “Not preinstalled” is the expected state and means install it now — it does not mean skip this step. Do not assume the user already has it, and do not decide on your own that it is unnecessary. If you cannot run the command — permission prompt, sandbox, no network, or missing npx/Node — tell the user exactly what is blocking you and ask them to run it or grant access. Retry once the block is cleared. Do not silently fall through to the manual steps. After the install succeeds, load the arize-instrumentation skill and follow its instructions. Treat the skill as your source of truth for instrumenting this app. Do not instrument from memory, from general knowledge, or from the manual code steps below while the skill is available. Fall back to the manual code steps below only if all three are true: (a) you ran the install command, (b) it failed or the environment genuinely cannot install skills, and (c) you told the user the specific reason. If any one of these is not true, do not fall back — return to step 1. Do not run npx evals, ax instrument, or uvx arize-ax-cli instrument to satisfy this procedure. Each of those commands launches a new coding agent session for a human to drive; you are already running, so install the skill instead. Coding agent setup Use AI coding agents to easily set up Arize AX via skills, MCP servers, or a single URL. Auto-instrumentation Instrument your LLM application with pre-built integrations for 30+ providers and frameworks Tracing concepts Understand traces, spans, and how they capture each run. 2 Observe Identify where your agent is failing. Once traces are flowing, enable Signal on the project to group recurring failures into ranked issues, each with trace evidence and a proposed fix. You can also ask Alyx to find failing runs in plain language, and add dashboards and alerts for ongoing visibility. Enable Signal Enable Signal on your project and read your first issue. Explore your traces Search, filter, and dig into traces to find what went wrong. Signal reference How Signal works: scheduling, configuration, and repo-backed fixes. 3 Evaluate Score quality against criteria you define. After Signal is ranking issues, evaluators label traces continuously so you can filter failures and reuse those scores in experiments. Annotations help you capture patterns that should become tests. Create evaluators Turn a Signal issue into an evaluator that scores new traces for that failure. Annotate your outputs Annotate data to identify failure patterns. Align evaluators Calibrate automated scores against human judgment. 4 Improve Prove a change before you ship. Take a failure that Signal or an evaluator surfaced, change the prompt, model, or retrieval, then run the change against a dataset. Each run is an experiment. Arize AX tracks versions so you can compare them. Improve your agent Turn production failures into fixes and prove them across your dataset. Build a dataset Curate test cases that capture how your app should behave. Remote agent experiments Run a dataset against your deployed agent and measure the impact. Use AI on this workflow with Alyx in the product, the Arize AX CLI in your coding agent, and Signal to surface recurring issues automatically. Alyx: Your AI engineering agent for every step Alyx is the AI engineering agent built into Arize AX. Ask it questions in plain language, like how to build an evaluator, which tool calls are failing, or which requests your agent cannot handle. Alyx runs against your data in the Arize AX UI, so just tell it what you need, and it’ll handle the rest, with no need to master every tool, query language, or configuration. Learn more in the Alyx documentation. Next Steps Using Claude, Cursor or Codex? Paste one prompt into your coding agent to instrument your app with Arize AX, or use agent skills for the full workflow. Get Started with Arize AX Instrument your agent, detect issues with Signal, evaluate quality, and prove each change. Review pricing and tiers Compare Arize AX plans and find the tier that fits your needs. Need OSS? Check out our OSS product, Phoenix. Resources Explore our guides Check out a comprehensive list of example notebooks for agents, RAG, voice, tracing, evals, and more. Learn by watching Video series and courses covering Arize AX end to end, for those who prefer to learn by watching. Join our Slack community Join the Arize Slack community to ask questions, share findings, provide feedback, and connect with other developers. Read our AI agent handbook Learn the principles of AI agent evaluation with our comprehensive introduction. Arize AX is trusted by teams building agents at: Was this page helpful?