Imagine a world where complex business processes, from medical coding to loan applications, could be automated with ease. That's the promise of Opus, a new AI framework designed to generate and optimize intricate workflows. Traditionally, Business Process Outsourcing (BPO) has been used to handle these detailed, often tedious tasks. But BPOs can be costly and inflexible. Opus offers a compelling alternative by using a Large Work Model (LWM) in conjunction with a Work Knowledge Graph (WKG) to create efficient, automated workflows. Opus works by first understanding the 'intention' behind a task – what needs to be done, given specific inputs and desired outputs. This intention is then used to query the WKG, a vast repository of industry best practices and operational knowledge. The LWM then crafts a workflow represented as a directed acyclic graph (DAG), where each node represents a task consisting of executable instructions. This initial workflow is then further optimized for cost and efficiency by considering various factors like time, resource usage, and task dependencies. In a head-to-head comparison with state-of-the-art LLMs in a medical coding scenario, Opus significantly outperformed the competition. Both the large and small versions of Opus showcased impressive results, demonstrating the potential of this framework to revolutionize industries reliant on complex workflows. While challenges remain, Opus represents a significant leap towards automating intricate business processes, offering a more cost-effective and adaptable solution than traditional BPOs. As the system evolves, its impact on various industries could be substantial, streamlining operations and unlocking new levels of efficiency.