As AI agents powered by large language models become more complex, developers often encounter erratic and unexpected behaviors during testing. From agents falling into infinite loops to models struggling with certain data formats, these issues can be tricky to diagnose and resolve. In this episode, Bradley Arsenault and Justin Macorin explore real-world examples of AI agents going off the rails. They discuss practical techniques like action governors, confusion matrix analysis, minimum task requirements, and targeted fine-tuning to create more robust and reliable agents. Tune in for valuable insights on taming unruly AI from two experienced practitioners at the forefront of prompt engineering and AI product development.

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