Sample AI traces at 100% without sampling everything

The text discusses the challenges and strategies related to sampling AI traces in monitoring tools like Sentry, focusing on head-based sampling where the decision to sample is made at the root of a trace, affecting all subsequent spans. It highlights that in AI applications, where each agent run can involve various tool calls and decision-making processes, sampling decisions must be carefully considered to avoid losing critical debugging information. The text explains that while lower sampling rates might be used due to cost concerns, the actual expense of AI API calls far exceeds that of observability costs. It suggests using a combination of full trace sampling for AI-related routes and emitting metrics and logs for every call as a fallback when 100% sampling is not feasible. It also touches upon setting up custom dashboards to track and analyze the cost and performance of AI operations effectively, emphasizing the importance of balancing detailed trace data with cost-efficient observability strategies.

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