Governed Agentic AI for Salesforce CRM Platforms: A Reference Architecture for Data Grounding, Decision Intelligence, Trust Controls, and Lifecycle Reliability
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P153Keywords:
Agentic AI, Salesforce CRM, Machine Learning, Customer Relationship Management, Generative AI, Data Cloud, Data 360, Retrieval Augmented Generation, CRM Governance, AI Trust Layer, Enterprise Architecture, Decision Intelligence, Software Reliability, AI Risk ManagementAbstract
Artificial intelligence and machine learning are increasingly being embedded into customer relationship management platforms to support predictive engagement, automated service resolution, intelligent sales guidance, and operational decision support. However, the transition from conventional AI enhanced CRM to agentic CRM platforms introduces new architectural and governance challenges. Modern Salesforce CRM environments are no longer limited to workflow automation or dashboard based analytics. They increasingly combine unified customer profiles, retrieval augmented generation, autonomous agents, low code workflow orchestration, predictive models, external enterprise integrations, and security controls that must operate across sales, service, marketing, commerce, and industry specific processes. The research problem addressed in this paper is the absence of a structured, vendor aware but technology neutral architecture for governing AI and ML driven Salesforce CRM platforms where customer data, generative AI agents, predictive decision models, and enterprise workflows interact under strict requirements for trust, privacy, reliability, explainability, auditability, and operational resilience. This paper proposes the Governed Agentic CRM Intelligence Architecture, a layered conceptual framework that integrates CRM data grounding, machine learning decision intelligence, retrieval augmented generation, agent orchestration, trust layer controls, human oversight, lifecycle governance, and reliability engineering. The contribution of this study is fourfold. First, it defines a reference architecture for AI driven Salesforce CRM platforms. Second, it identifies the practical gaps between conventional CRM automation and agentic CRM decision systems. Third, it connects AI governance, cybersecurity, software reliability, and CRM platform design into a unified enterprise model. Fourth, it presents implementation considerations, limitations, and future research directions for scalable and trustworthy AI adoption in Salesforce CRM ecosystems. The paper is conceptual and architecture based; therefore, it does not claim experimental proof. Instead, it provides an academically grounded design model that can guide enterprise architects, CRM platform teams, AI governance leaders, and researchers investigating responsible AI in customer centric platforms.
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