A cautionary pattern from the article: semantic layer YAML files were last updated by a data team member who left the company, are no longer used by BI tools, and don’t include two new product lines launched since. Static context rots. Data systems are never static, and the context layer shouldn’t be either — data sources change upstream, business requirements evolve, and agents encounter novel situations that expose gaps.
The solution is self-updating context flows: when a data agent provides incorrect data and requires accuracy refinement, that correction feeds back into the context layer. This creates a living, constantly evolving corpus. The pattern connects to Agents that store error patterns learn continuously without fine-tuning or retraining — error-driven learning applied to the context layer itself, not just agent behavior. It also reinforces Compound engineering makes each unit of work improve all future work: each agent correction compounds the context layer’s accuracy for all future queries. A survey of 10+ knowledge graph architectures confirms this is the hardest problem in the space: Knowledge evolution is the biggest unsolved problem across all graph architectures shows that most systems are append-only, and even the best only add links without pruning, merging, or detecting contradictions.