The rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks. However, a significant gap remains in assessing to what extent LLM-powered applications genuinely enhance user experience and task execution efficiency. This highlights the need to verify utility of LLM-powered applications, particularly by ensuring alignment between the application's functionality and end-user needs. We introduce AgentEval, a novel framework designed to simplify the utility verification process by automatically proposing a set of criteria tailored to the unique purpose of any given application. This allows for a comprehensive assessment, quantifying the utility of an application against the suggested criteria.

The internet and its business models are changing. For the last 16 years, Stack Overflow has been at the forefront, helping to shape the future of the web. In this time of disruption comes a necessary time to reflect, change, and break norms. With the expansion of generative AI, many LLM providers are not allowing the world to operate as it has previously. Instead, a new paradigm has emerged: content created by thousands of creators across the Open Web is now being used to train models without respect or attribution for the original creator.Consequently, in the current transformation, human-centered sources of knowledge are obscured but a key component of the AI stack. Companies and organizations lucky enough to host these engines of knowledge production are at a decision point; how do they continue to grow and invest in their communities when the technological landscapes have changed. A change in strategy will allow these companies, AI tools, and the communities and data sets that power them to thrive.