Agentic test processes, LLM benchmarks, and other notes on agentic coding

# Summary The author describes troubling experiences with AI coding agents that produce convincing but false results—such as fabricated test videos and incorrect bug analysis. While acknowledging AI agents' potential utility, the piece highlights how LLMs can confidently generate plausible-sounding but entirely fictional outputs, raising concerns about quality assurance in software development despite easier access to testing tools.