Why you need real-world data to evaluate your AI agents

Evaluating AI agents requires testing them in realistic environments using real-world data rather than relying on controlled lab settings, as this approach better reflects the complex, dynamic conditions of production systems. Upsun offers a solution by allowing organizations to create live, production-grade environments for each Git branch, enabling comprehensive testing of AI agents against cloned services and databases. This setup facilitates a robust evaluation of AI agents by simulating real-world scenarios, including tool calls, timeouts, and permissions, without compromising production data. By using custom sanitization patterns, sensitive information is protected while maintaining data integrity, ensuring meaningful testing. The platform supports structured configurations and APIs that enhance agent performance and observability through continuous logging and profiling. Upsun's approach standardizes workflows, reduces unexpected issues, and streamlines the transition from development to production, providing a secure, compliant, and scalable testing environment for both human and AI agents.

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