Shipping With Context using Knowledge / Context Graphs

AI-generated code faces challenges in software delivery due to a lack of context, not model quality, highlighting the importance of a cohesive system that reflects the interrelationships between various components like pipelines, environments, and policies. Knowledge graphs are proposed as a solution to transform fragmented DevSecOps data into operational truth, emphasizing that overmodeling, undermodeling, and stale context can hinder their effectiveness. AI-assisted DevOps is seen as a preliminary stage where AI helps with tasks like code writing and log summarization, while AI-operational DevOps aims for AI to understand and manage the entire software delivery process. The text argues for the necessity of a shared context layer to allow AI to operate effectively, suggesting that operational reasoning, rather than mere automation, is the goal. The critical role of freshness and relevance in maintaining effective knowledge graphs is stressed, advocating for a focus on minimal, use-case-driven modeling that addresses real-time needs.

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