How to build an AI agent for your CRM

AI systems often struggle due to unreliable data, particularly in CRM systems, where the focus has traditionally been on storing records rather than understanding relationships and conversations, leading to ineffective "AI for sales" features. Effective CRM systems should integrate meeting intelligence as structured inputs to improve automation and enhance the accuracy of revenue forecasts, as deals are dynamic processes influenced by various factors like timing and objections, not linear progressions. Current CRMs often fail to capture this complexity, resulting in data drift that undermines trust in forecasts and AI reasoning. A solution lies in developing CRM agents with relational memory that can track the nuances of email and meeting interactions to provide actionable insights, thereby transforming pipelines from static logs into dynamic systems. By incorporating tools like Nylas Calendar APIs, which streamline scheduling and coordination, CRM systems can maintain deal velocity and communication infrastructure, ensuring AI agents function effectively under real-world conditions.