How does your company measure the impact of agents and skills in real production, not just benchmarks?
Summary
A discussion on how companies should measure the real-world impact of AI agents and skills in production environments, rather than relying solely on benchmark results.
Similar Articles
Your AI Agent Scores Well on Benchmarks. So Why Does It Still Fail in Production?
The article discusses the benchmark reality gap in AI agents, where high benchmark scores do not guarantee reliable performance in real-world production environments, emphasizing the need for better evaluation metrics.
How are you keeping track of what your AI agents are actually doing in production?
The article highlights the challenges teams face in monitoring AI agents in production, particularly regarding compliance and health, and invites discussion on current practices and gaps.
anyone actually running AI agents in production for client work? or still demo-ware?
A discussion questioning whether AI agents are truly being used in production for client work or if they remain mostly demos, reflecting on the gap between hype and real-world reliability.
How are people evaluating AI agents after they go into production?
The article discusses methods and challenges for evaluating AI agents in production environments, focusing on quality assurance for real-world conversations beyond pre-defined evaluation sets.
How are you evaluating AI features in production?
A discussion on the methodologies and challenges involved in evaluating AI features once they are deployed in production environments.