Semantic overload: why AI agents get facts wrong

This post identifies and explains the problem of 'semantic overload' in AI agents, where an excess of similar but potentially contradictory or outdated information leads to factual errors. It details specific failure modes like context poisoning, distraction, confusion, clash, and context rot. The post critically analyzes the limitations of vector search alone, particularly its inability to reason over explicit relationships between entities, handle temporal blindness, or correctly interpret negation. It also describes the 'relational gap' in current agent memory systems, leading to contradictions, lack of provenance, and scalability issues. Finally, it proposes and discusses four approaches to mitigate semantic overload: hybrid search, re-ranking with cross-encoders, knowledge graphs/GraphRAG, and structured, graph-based memory, emphasizing the need for a unified context layer.