Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale

Abstract

As AI agents increasingly rely on memory systems to align with user intent, updating these memories presents challenges of semantic conflict and ambiguity. Inspired by impact analysis in software engineering, we introduce SemanticCommit, a mixed-initiative interface to help users integrate new intent into intent specifications - natural language documents like AI memory lists, Cursor Rules, and game design documents - while maintaining consistency. SemanticCommit detects potential semantic conflicts using a knowledge graph-based retrieval-augmented generation pipeline, and assists users in resolving them with LLM support. Through a within-subjects study with 12 participants comparing SemanticCommit to a chat-with-document baseline (OpenAI Canvas), we find differences in workflow: half of our participants adopted a workflow of impact analysis when using SemanticCommit, where they would first flag conflicts without AI revisions then resolve conflicts locally, despite having access to a global revision feature. Additionally, users felt SemanticCommit offered a greater sense of control without increasing workload. Our findings indicate that AI agent interfaces should help users validate AI retrieval independently from generation, suggesting that the benefits from improved control can offset the costs of manual review. Our work speaks to the need for AI system designers to think about updating memory as a process that involves human feedback and decision-making.