Arize provides an end-to-end platform that helps you observe, evaluate, and continuously improve your agents by:

1. Instrumenting Your Agent
   • Arize automatically captures all the traces and spans—every step in your agent’s workflow—so you can see exactly how the agent is making decisions.
   • This gives you a single place to review everything from the LLM prompts and responses to your router’s tool choices and skill outputs.
2. Centralizing Observability Data
   • Each agent run (or “trace”) streams into Arize, giving you a holistic, real-time view across all runs—helpful for debugging or spotting performance degradations.
   • Arize stores all these traces, along with any custom metrics or additional context (e.g., user IDs, timestamps, cost per run).
3. Built-in Evaluation and Labeling
   • You can attach “evals” to specific parts of the agent’s execution (e.g., skill correctness, function-calling accuracy, or code generation success).
   • Arize lets you run both code-based checks and LLM-as-a-judge evaluations. You can also add human annotations (labels) directly in the platform, so you get a single source of truth for performance data.
4. Dashboard & Visual Debugging
   • Traces show up in an interactive UI, so you can drill down step by step—for example, verifying whether the router chose the correct tool or if the agent’s output was coherent.
   • You can filter or group runs by model version, input type, or any custom tags, letting you slice and dice to discover patterns or isolate failing scenarios.
5. Experiment Management
   • You can set up experiments by defining a batch of test queries or new agent versions, running them through the system, and comparing results side by side in Arize.
   • This is key for evaluation-driven development: each new version is tested against your curated dataset, ensuring you can track regressions and gains with actual metrics.
6. Production Monitoring & Continual Improvement
   • In production, Arize can capture real user traffic, highlight anomalies, and feed new “edge cases” back into your test dataset.
   • This closes the loop so you can keep refining prompts, routes, or model choices whenever user behavior or system performance signals it’s necessary.