How Alibaba Cloud’s Agentic Search Redefines Enterprise AI Search

From Keyword Matching to Agent‑Native Search

As large‑model technology becomes mainstream, enterprise search is evolving from traditional keyword matching to interactive Agent‑centric search. The core challenge is upgrading search capabilities without sacrificing stability or cost control.

01 Agent‑Native Architecture and Knowledge‑Lake Concept

Agent‑native experience : Alibaba Cloud Elasticsearch now supports native Agent creation, orchestration, and usage, enabling Agents to perform ES operations such as cluster management, data retrieval, and analysis.

Agentic Search : Search results are returned as JSON or Markdown, formats that AI agents can directly consume, reducing token consumption and improving execution efficiency.

Agentic data processing : A built‑in multimodal data‑processing Agent converts natural‑language data‑processing requests into offline tasks, builds indexes after processing, and stores interaction logs, user preferences, and Skills for long‑term memory.

Full‑lifecycle Skills : Instance creation, cluster configuration, health diagnostics, monitoring, and alerting are abstracted as reusable Skills, allowing various Agents (e.g.,悟空, QoderWork, Dataworks Data Agent, OpenClaw) to manage Elasticsearch resources with minimal friction.

02 Enterprise‑Level "Knowledge Memory Lake"

By adopting the Agentic Search architecture, Elasticsearch becomes a long‑term memory and knowledge‑storage engine. It stores interaction logs, user preferences, and Skills, enabling a "memory that gets smarter with use" which cuts LLM token usage and boosts task success rates.

03 High‑Performance Foundations

FalconSeek engine : A self‑developed engine that improves vector query performance by 50‑300% and, combined with GPU acceleration and BBQ quantization, delivers millisecond‑level context retrieval even at trillion‑scale data.

04 Hybrid Retrieval 2.0 and Cost‑Effective Indexing

Hybrid Retrieval 2.0 replaces separate engines with a unified framework that performs multi‑path recall and Reciprocal Rank Fusion (RRF) in a single pipeline, eliminating the "no results after filtering" issue.

Native unified retrieval : Multi‑path recall + RRF fusion within one architecture.

Search‑while‑filtering : Applies filters and similarity thresholds during KNN search, solving the engineering pain point of empty results after filtering.

Dynamic RRF fusion : Semantic‑aware weight adjustment and Learned Sparse Retrieval (LSR) automatically balance recall quality without manual tuning, especially improving long‑tail knowledge recall.

Logical hot‑cold index separation : Only 10% of hot data receives high‑performance HNSW indexing; 90% cold data uses low‑cost storage, reducing node memory by 70% and halving compute specs.

BBQ quantization : Asymmetric quantization compresses 10 billion vectors, shrinking storage nodes from 225 to 11 and saving up to 95% of resources; combined with OpenStore storage‑compute separation, overall TCO drops >40%.

05 Search‑as‑Execution: Agentic RAG

Agentic RAG builds a three‑part index (text, vector, structured) for knowledge bases. Continuous learning feeds retrieval quality back into the index, forming a closed‑loop that improves both indexing and search performance.

Best‑Practice Three‑Step Path

Rapid build : Deploy BM25 + kNN + RRF pipeline.

Effect optimization : Integrate Baidu‑Lian Embedding/Rerank and BBQ quantization.

Extreme performance : Leverage FalconSeek engine and storage‑compute separation.

This approach has powered Jinshan’s trillion‑scale semantic document search and a major model provider’s real‑time C‑end retrieval.

Ecosystem Collaboration

Alibaba Cloud and Elastic co‑developed ES Skills, exposing instance management, cluster diagnostics, index management, and data queries as standardized tools callable by AI agents via natural language.

Cloud‑native architecture enhancements : Compatibility with Elastic’s latest features (Vector Search, ML Nodes) plus OpenStore storage‑compute separation and Serverless for on‑demand scaling.

Full‑stack observability : CloudLens for ES monitors CPU, memory, disk, slow queries, health events, and vector latency, providing proactive alerts and root‑cause analysis.

Future Directions

Alibaba Cloud plans to deepen three areas: (1) AI‑search evolution toward an "Agentic Memory Lake"; (2) performance breakthroughs via FalconSeek upgrades and storage‑compute separation; (3) industry‑specific solutions such as compliant financial instances, high‑concurrency e‑commerce, and multimodal media workloads.

Overall, the platform aims to deliver stable, efficient, intelligent, and cost‑controlled AI search infrastructure that serves as the foundation for next‑generation AI Agent applications.