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This article critically examines the accuracy of AI visibility tools that claim to measure brand presence in generative AI responses, arguing that they provide false precision due to nondeterminism, personalization, and scraping biases. It calls for transparency in methodology and warns against treating opaque dashboards as stable truth.
A discussion on the challenges of testing AI agent harnesses with non-deterministic components, exploring approaches like golden output diffing and using an LLM as a judge, while questioning the validity of such methods.
This article argues that standard test suites with fixed inputs and expected outputs are insufficient for AI agents due to infinite input spaces and non-deterministic behavior, advocating for property-based testing instead.