Generative AI represents a significant shift in software engineering, transitioning from rule-based systems, which prioritize reliability and determinism, to creative systems that prioritize flexibility and can produce varying outputs. While traditional systems ensure that identical inputs yield identical outputs, generative AI's strength lies in its creativity, which inherently introduces non-determinism and variability. This raises critical considerations for applications where reliability is paramount, such as compliance and revenue-impacting processes. The development of these creative systems poses new industrial challenges, including issues of speed, cost, and predictability. To address these challenges, a robust framework, termed the Agent Development Lifecycle, has been established, emphasizing reliability through layered AI models and allowing non-experts to define agent behaviors easily. The goal is to create robust, reliable AI systems that leverage the innovative potential of generative models while maintaining the necessary standards of software dependability.

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