Agentic AI for Customer Service and Contact Center Solutions

Abstract

Agentic Artificial Intelligence systems represent a transformative evolution in customer service automation, moving beyond traditional rule-based architectures toward autonomous, reasoning-capable agents that demonstrate emergent behaviors through foundation model integration. This technical review evaluates the conceptual foundations, architectural frameworks, and practical implementations of agentic systems across customer service environments. The evaluation encompasses five major frameworks, including Auto-GPT, LangChain Agents, CrewAI, OpenAgents, and MetaGPT, examining their capabilities in planning, collaboration, tool integration, and scalability. Contemporary implementations demonstrate sophisticated multi-layered reasoning systems that leverage Retrieval-Augmented Generation for dynamic knowledge access while maintaining contextual coherence across extended customer interactions. Critical architectural components include task planners, memory engines, tool orchestration mechanisms, and multi-agent coordinators that enable distributed processing across specialized roles. However, significant challenges persist in formal verification methods, failure mode characterization, and standardized evaluation protocols. The review identifies substantial gaps in theoretical foundations for autonomous decision-making processes, particularly regarding the mathematical formalization of agent behaviors and comprehensive economic modeling. Implementation considerations reveal complex trade-offs between system sophistication and operational requirements, where scalability demands sophisticated memory management strategies and fault-tolerance mechanisms. Future directions emphasize multi-agent specialization, federated knowledge systems, and human-centered design principles essential for enterprise adoption.