The international digital advertising landscape is facing the dual trends of data transformation driven by privacy regulations and media migration driven by video and social networks. Small and medium-sized enterprises engaged in cross-border advertising often face challenges such as labor shortages, lack of experience, and mismatched tools, making it difficult for them to effectively utilize budgets, accurately test creatives, and flexibly adjust bids. To address this challenge, we created a conversational advertising agency implementation framework for small and medium-sized enterprises. This framework integrates platform policies, industry vocabulary, and merchant knowledge, and then uses retrieval-augmented generation (RAG) technology and tooled functions to conduct A/B testing coordination of budget, audience, keyword, and creative. Multi-objective reinforcement learning (PPO/DPO) is used to comprehensively optimize ROAS, CPA, experience (CSAT, AHT), and compliance risks. This research dataset contains anonymous multi-source samples from countries such as the United States, the United Kingdom, and Germany, covering many fields such as search, social, video, and display. A hierarchical training mechanism was established, and an online grayscale testing process was implemented. Compared with the rule template RAG benchmark, the system achieved significant improvements in intent recognition, initial resolution, and tool call success rates. After one to two weeks of tiered A/B testing, the system achieved the combined effects of reduced cost-performance, increased return on investment, and shortened average processing time, facilitating comparison and re-verification. The article publishes the main formulas for intent loss, conversion estimation, PPO target, budget rhythm penalty, and bid multiplication update, with relevant statistics and comparison charts attached.

Jing Zheng. Design and Optimization of AI Intelligent Dialogue Agents for Small and Medium-Sized Enterprise Advertising Scenarios. International Journal of Big Data Intelligent Technology (2025), Vol. 6, Issue 2: 114-122. https://doi.org/10.38007/IJBDIT.2025.060212.

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