Jamba, a hybrid model developed by AI21 Labs, is a significant advancement in enterprise GenAI. It combines the efficiency of Mamba's state-space layers with Transformer attention's precision, enabling processing up to 256k tokens of context and redefining enterprise applications like financial document synthesis and long-form data analysis. Jamba represents a pivotal shift in GenAI, driven by hard-won lessons and deliberate design choices. Its success showcases the potential of novel architectures, thoughtful design, and enterprise-grade execution, but also highlights challenges such as accuracy, scalability, and dynamic adaptability that remain to be addressed. With its open-source foundation and flexible deployment options, Jamba empowers use cases like automating tedious tasks like term sheet generation, while paving the way for the future of agentic workflows with true AI agents capable of adapting to complex tasks.