Agent Architecture and Practice: Building the Next‑Generation Recommendation and Search Systems

The piece first outlines Alibaba Cloud AI Search’s Agentic RAG solution, which addresses high‑concurrency, multimodal data, and multi‑hop query scenarios. It describes the progression from a single‑agent to a multi‑agent system where planning, retrieval, and generation modules cooperate to understand complex intents.

The multi‑path retrieval layer combines vector, text, database, and graph recall strategies to boost coverage and accuracy. GPU acceleration is quantified for both indexing and query stages, and extensions such as NL2SQL and multimodal search are detailed.

Next, Huawei Noah’s analysis of recommendation system evolution is presented, tracing the shift from deep‑learning models to large language models (LLM) and AI agents. The article explains list‑type versus dialogue‑type recommendation, the KAR project’s factorized prompting, and a multi‑expert knowledge adapter that maps semantic knowledge into recommendation embeddings, reporting an AUC lift of 1.5% in online A/B testing.

The Baidu GRAB model is then examined as a generative ranking approach for ad recommendation. It leverages the LLM scaling law and Transformer architecture, introduces a Q‑Aware RAB causal attention mechanism for adaptive modeling of complex interactions, and adopts a two‑stage STS training pipeline, heterogeneous token representations, dual‑loss stacking, and KV‑Cache to sustain high‑throughput inference, with quantified business gains after full deployment.

The full ebook provides architecture diagrams, detailed performance evaluations, and additional case studies; readers can obtain it via the QR code shown in the original source.