How Agentic Architecture Powers Next‑Generation Recommendation and Search Systems

The piece summarizes several advanced AI‑driven search and recommendation solutions presented in the e‑book "Intelligent Agent Architecture and Practice: Building the Next‑Generation Recommendation and Search Systems".

Alibaba Cloud AI Search – Agentic RAG : Based on a technical talk by Xing Shaomin, the article outlines challenges such as high concurrency, multimodal data, and multi‑hop queries. It describes the evolution from a single‑agent to a multi‑agent system, where planning, retrieval, and generation modules cooperate to understand complex intents. The multi‑path retrieval chain mixes vector, text, database, and graph recall to improve coverage and accuracy. GPU‑accelerated indexing and query quantization are compared, showing measurable speed‑up, and extensions like NL2SQL and multimodal search are discussed.

Huawei Noah – LLM‑Enhanced Recommendation : The article reviews the transition from deep‑learning recommenders to large language model (LLM) and AI‑agent eras. It identifies core challenges—noisy implicit feedback, limited semantic understanding, and difficulty mining user intent. Using the KAR project as a case study, it explains factorized prompting and a multi‑expert knowledge adapter that maps semantic knowledge into the recommendation embedding space. The design balances text feature dimensionality with real‑time latency. Further sections cover dialogue‑style recommendation, LLM prompting and fine‑tuning strategies, and a multi‑capability AI‑agent coordination architecture, reporting an AUC lift of 1.5 % from online A/B testing.

Baidu – GRAB Generative Ranking for Ads : The article details Baidu's generative ranking model GRAB, which replaces heavy feature engineering in traditional DLRM pipelines. By applying LLM scaling laws and a Transformer‑based end‑to‑end sequence model, user behavior and ad targets are jointly encoded. A novel Q‑Aware RAB causal attention mechanism captures complex interactions and temporal signals. The paper also describes the STS two‑stage training algorithm, heterogeneous token representations, dual‑loss stacking, and KV‑Cache optimizations for high‑throughput inference, along with quantified business benefits after full deployment.

Elasticsearch Vector Search & RAG : Finally, the article introduces a practical guide to building vector search and Retrieval‑Augmented Generation (RAG) applications with Elasticsearch, illustrating how dense vectors are indexed and queried to support downstream LLM‑driven workflows.

Each section provides architecture diagrams, performance evaluation data, and references to the original technical reports, enabling readers to understand the problem statements, design choices, trade‑offs, and empirical results behind these next‑gen AI search and recommendation systems.