Building an LLM-Based Agent Platform for Enterprise Commercialization: Strategies, Architecture, and Practical Insights

This article outlines the strategic development of SalesCopilot, an LLM-based agent platform tailored for Kuaishou's B-end commercialization business. By focusing on Retrieval-Augmented Generation (RAG) and Agent technologies, the team addresses enterprise challenges such as fragmented knowledge, slow problem resolution, and limited operational support resources.

The platform adopts a "three horizontal, one vertical" architecture. The foundational AI Engine integrates RAG capabilities and business intent modules, supported by a dedicated evaluation center. The middle ChatHub layer provides a scalable framework for intelligent customer service, while the top Business Application layer enables tenant-specific customization. Vertical plugin and multi-tenant frameworks ensure system stability, data isolation, and extensibility across diverse business scenarios.

RAG implementation is structured into offline and online pipelines. The offline phase focuses on knowledge construction, preprocessing, and multi-path indexing (vector, ES, and GraphRAG), significantly expanding document coverage and cross-document reasoning. The online phase executes retrieval, augmentation, and generation, with continuous optimization of query understanding and recall strategies to overcome inherent LLM limitations like hallucinations, outdated knowledge, context constraints, and data security risks.

Agent technology extends beyond simple tool use by establishing a robust schema for intent recognition and execution. The system supports both single-plugin direct mapping and multi-plugin orchestrated workflows, balancing predefined execution logic with dynamic reasoning. Key design principles include pluggable model architectures, model-specific prompts (LSP), and quantization for efficient CPU deployment, all rigorously validated through a comprehensive evaluation center to manage AI uncertainty.

Key reflections emphasize the democratization of AI technology, the multiplicative impact of meticulous RAG optimization, the strategic advantage of starting with vertical domains before scaling, and the inevitable industry shift toward multimodal interactions. These insights provide a practical, engineering-focused roadmap for enterprises navigating the complexities of LLM application development.