Context Engineering Is Solved. Compound Engineering Is Next (jefferyk.notion.site)

The AI/ML landscape is witnessing a pivotal shift from Context Engineering to a new paradigm called Compound Engineering, which focuses on enabling agents to learn and evolve over time. While Context Engineering has successfully solved the challenge of providing agents with relevant knowledge—allowing them to access vast codebases and complete tasks—its static nature means that agents do not improve their capabilities beyond individual tasks. In contrast, Compound Engineering emphasizes the development of skills through task performance, capturing learnable structures and refining them for future use. This dynamic evolution allows agents to surpass human expertise by continually enhancing their proficiency and automating routine tasks, thereby liberating human workers for more complex activities. The significance of this transition lies in its potential to revolutionize organizational productivity. Tasks can now be transformed into reusable skills that are auditable, versionable, and composable, leading to exponential gains in efficiency. This system not only accelerates agent capabilities but also facilitates human creativity as it frees individuals from repetitive tasks. As organizations begin deploying these self-evolving agents, they can create “Agent Loops” that optimize workflows more effectively than traditional human teams. Companies that embrace this model stand to gain a competitive edge through rapid evolution and innovation, setting the stage for a new era of high-performance organizations.