The Era of Agentic Workflows (and why 80% reliability is a failure) (project-1960fbd1.doanything.app)

A new discussion is emerging around the concept of "agentic workflows" in AI, emphasizing the need for systems that exceed 80% reliability, which many in the AI/ML community now view as inadequate. This shift highlights the growing expectation for AI systems to operate not just effectively but with a higher degree of reliability and autonomy. As AI applications become increasingly integrated into critical functions, the call for robust performance underscores the need for continuous improvement, innovation, and testing of these technologies. The implications of this trend are significant. Developers and researchers are urged to push the boundaries of current model architectures and frameworks, striving for advancements that ensure greater trust and dependability in AI operations. By focusing on achieving reliability above the typical thresholds, the AI/ML community can foster the development of smarter, more resilient technologies that can handle complex tasks with minimal human oversight. This movement not only pushes for heightened technical capabilities but also aims to reshape the standards and expectations for AI applications in various industries.