Building an AI Agent that Thrives in the Real World

Building an AI agent involves complexities such as testing, iterating, and improving its performance. Tools like Arize and Phoenix are essential for navigating these challenges. During the development phase, Phoenix traces provide valuable insights into how users interact with the AI agent, enabling quick identification of issues and iteration. Once in production, Arize becomes crucial for monitoring user interactions and ensuring the AI agent performs as expected. Daily usage of dashboards helps track high-level metrics such as request counts, error rates, and token costs. Experiments are useful for testing changes like model updates or A/B tests, while datasets help identify patterns and form hypotheses. Automating evaluation workflows using CI/CD pipelines ensures thorough testing with minimal manual effort. Continuous monitoring and troubleshooting involve identifying issues through evals and resolving them in the Prompt Playground before pushing changes to production.

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