Structural Challenges in Enterprise Agents - Enterprise agents frequently fail due to three core structural issues: ambiguity in data source selection, staleness of context, and misalignment with user preferences [2][3][4] - Ambiguity arises from the lack of a defined "source of truth" among various knowledge bases, leading to uncertainty in selecting the correct tables or metrics [3][6] - Staleness occurs because business processes, KPIs, and definitions evolve rapidly, rendering static documentation and knowledge bases obsolete [4][12] - Preference subjectivity remains a significant hurdle, as different teams often utilize distinct calculation methods or filters for the same business metrics [17][18] Framework for Optimization - A hierarchical "source of truth" structure is recommended, prioritizing the semantic layer (curated KPIs/definitions) and canonical tables over complex database graphs [7][8][9][10] - Implementing a tiered approach—focusing on the semantic layer and canonical tables first—can resolve approximately 80% of data agent issues, leaving the remaining 20% for database graph integration [11] - A robust context life cycle must include live data sources (e.g., GitHub, CRM, DBT) and a mandatory feedback loop to capture user-reported errors and process updates [13][14][15] - Performance tracking requires a dedicated evaluation suite, utilizing either human-annotated questions or automated comparisons between agent outputs and historical ground truth [15][16] - Future research must address the "preference" problem by developing mechanisms to route agents to specific metrics based on individual or team-level context, effectively creating a "hive mind" for enterprise data [19][22][24]