How Agentic Architectures Power Next‑Gen Recommendation and Search Systems

The article first examines Alibaba Cloud AI Search’s Agentic RAG solution, which tackles high‑concurrency, multimodal data, and complex multi‑hop queries. It describes the evolution from a single‑agent to a multi‑agent system, detailing how planning, retrieval, and generation modules cooperate, and how a mixed retrieval chain—combining vector, text, database, and graph recalls—improves coverage and accuracy. GPU‑accelerated indexing and query quantification are also discussed.

Next, it reviews Huawei Noah’s analysis of recommendation‑system evolution, moving from deep‑learning models to large language models (LLM) and AI agents. The author outlines core challenges such as noisy implicit feedback, limited semantic understanding, and difficulty mining user intent. By treating LLMs as feature enhancers and integrating them via factorized prompting and multi‑expert knowledge adapters, the system achieves efficient mapping of semantic knowledge into recommendation embeddings. Design trade‑offs between text‑feature dimensionality and real‑time constraints are explained, and experimental results show a 1.5% AUC lift and positive online A/B‑test outcomes.

The third case study focuses on Baidu’s GRAB (Generative Ranking for Ads) model, which replaces traditional feature‑engineering‑heavy DLRM pipelines with an end‑to‑end generative sequence model based on LLM scaling laws and Transformer architecture. The Q‑Aware RAB causal attention mechanism adapts to query‑aware relative bias, capturing complex interactions and temporal signals. The paper details the STS two‑stage training algorithm, heterogeneous token representations, dual‑loss stacking, and KV‑Cache optimizations that ensure high‑concurrency online inference. Reported business metrics demonstrate significant gains after full deployment.

Collectively, these sections provide a comprehensive technical roadmap—including architecture diagrams, performance evaluations, and implementation nuances—for building the next generation of recommendation and search systems.