你的資料很薄弱：為什麼沒有這個基礎設施修復AI智能體就會失敗
xebia首席技術官niels zeilemaker強調，代理ai的成功完全取決於資料的可用性。如果沒有強大的擷取基礎，ai智能體就無法有效地擴展以自動化業務流程。
xebia global cto niels zeilemaker outlined the prerequisite infrastructure required for organizations to successfully deploy and scale ai agents. he argues that while agents offer process acceleration, their success is contingent on making internal data accessible and consumable by ai systems. zeilemaker emphasized that agentic ai scales on data strength, implying that current data architectures are often insufficient for autonomous workflows. this perspective challenges the rush to deploy advanced models, suggesting that data engineering is the primary bottleneck for enterprise ai adoption. companies prioritizing agent capabilities over data readiness may face failed deployments as agents lack the necessary context to operate.