Trustworthy Agentic Intelligence for Enterprise CRM: A Unified Architecture for Grounded AI, Predictive Decision Automation, Retrieval Augmentation, and End-to-End Observability
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I1P167Keywords:
Agentic AI, Customer Relationship Management, Retrieval-Augmented Generation, Predictive Analytics, Explainable AI, Governance, Observability, Large Language Models, Enterprise AutomationAbstract
Enterprise customer relationship management (CRM) systems are increasingly expected to combine natural-language interaction, predictive analytics, enterprise knowledge retrieval, and transactional automation. These capabilities are commonly integrated as separate services, creating a trust gap between what an artificial intelligence (AI) component recommends and what an enterprise system is permitted to execute. This paper proposes the Unified Trustworthy Agentic CRM Architecture (UTACA), an evidence-centered architecture that separates retrieval, predictive inference, agentic planning, policy validation, execution, human escalation, and observability. The central design principle is that a probabilistic model may propose an action but cannot authorize a consequential operation. Authorization is performed by a deterministic governance boundary using identity, evidence sufficiency, policy, confidence, and tool-risk constraints. The architecture also defines an AI decision transaction that records source provenance, retrieval context, model and prompt versions, predictive outputs, policy decisions, tool calls, execution responses, and human interventions. A reproducible evaluation protocol is specified for grounding fidelity, action correctness, policy compliance, trace completeness, failure containment, latency, and escalation quality. Because no new production experiment is asserted in this manuscript, the paper reports architectural propositions and an empirical validation design rather than fabricated performance claims. The resulting framework provides a rigorous basis for building and evaluating agentic CRM systems whose decisions are grounded, governable, auditable, and operationally observable.
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