How Baidu’s AI‑Powered Architecture Transforms Network Operations

Overview

The article introduces Baidu Intelligent Cloud’s end‑to‑end solution for network intelligent operations (NetOps), highlighting a three‑layer AI architecture that integrates multi‑source data, AI‑ready tool components, and scenario‑driven services.

Three‑Layer Architecture

AI Infrastructure Layer : consolidates data, provides essential AI toolkits, and supports downstream services.

AI Agent Core Layer : built on large models to achieve task understanding, tool invocation, and autonomous decision‑making.

AI Business Scenario Layer : packages reusable intelligent services for specific operational needs.

Key Practice Outcomes

The system delivers two major results:

Network Digital Engineer : AI Agents automatically perform high‑value tasks such as fault diagnosis, change verification, and health inspection.

Data Intelligence : Enables natural‑language cross‑domain data analysis, root‑cause reasoning, and automatic generation of visual dashboards.

Field tests show a dramatic reduction in manual interventions, faster response times, and higher decision quality, establishing a scalable engineering paradigm for AI‑native operations.

Motivation

Network engineers face 24‑hour alarm storms, complex CLI commands, and endless configuration checks. Traditional "experience + human effort" models cannot keep up with massive device counts, high‑throughput ports, and stringent service‑level expectations.

Why Intelligent Operations Are Essential

With Baidu’s expanding network scale and rising customer demands, NetOps must evolve from manual, experience‑driven processes to AI‑driven automation. Four evolutionary stages are identified, culminating in AI‑powered AIOps as the sole viable breakthrough.

Three‑Layer Architecture Details

3.1 AI Infrastructure Layer

This foundation comprises three core components:

Data : Optimized CMDB data (devices, SysLog, topology, IP) to ensure reliable AI outputs.

Tools : A suite of automated utilities (monitoring, fault localization, mitigation, inspection, change) registered in the MCP Server for direct LLM or Agent invocation.

Workflow : The Baidu Network Engine (BNE) built on BPMN provides drag‑and‑drop online process orchestration. By embedding Multi‑Agent frameworks, BNE supports dynamic, logic‑driven Agentic workflows.

3.2 AI Agent Core Layer

Built atop the infrastructure, the core layer implements a full pipeline:

Intent Recognition (Host Agent + Network RAG) : Uses a general LLM for natural‑language understanding and a network‑specific Retrieval‑Augmented Generation (RAG) to avoid hallucinations.

Task Planning (Plan‑Act Framework) : The Planner Agent decomposes complex requests into executable steps; the Actor Agent schedules sub‑Agents or tools, monitors results, and dynamically replans when failures occur. Deepseek‑R1 serves as the reasoning model.

Tool Execution (ReAct Framework) : Sub‑Agents invoke specific tools (e.g., quality data queries, log retrieval) in a "think → act → observe" loop, with a standardized plugin‑compatible execution engine.

3.3 AI Business Scenario Layer

Any network engineer can quickly build scenario‑specific sub‑Agents, enabling low‑cost, organization‑wide intelligent transformation. Example scenarios include:

Fault diagnosis and closure – turning expert knowledge into team capability.

Data visualization and analysis – one‑click generation of charts or large‑screen dashboards.

Consultation services – natural‑language queries for IP classification, device info, etc.

Delivery acceleration – real‑time status checks for link testing or equipment rack‑up without manual CLI.

NetStudio: The AI‑Native Workbench

NetStudio is positioned as an AI‑first unified workbench for network professionals, combining VS Code‑level tooling with LLM‑driven assistance (similar to Cursor or Baidu Comate). It replaces traditional CLI/GUI interactions with a language‑user interface (LUI), enabling:

One‑sentence device data visualization (status, traffic, logs, topology).

Cross‑domain data correlation (e.g., OTN fault impact on business‑layer switches).

Intelligent log parsing and statistical analysis via natural language.

The architecture integrates the three‑layer framework, with Scenario Agent, Planner Agent, Act Agent, and Conclusion Agent orchestrating end‑to‑end AI services.

Real‑World Deployments

Use Case 1: Fault Diagnosis

Agentic workflows automate data collection, analysis, and remediation for data‑center faults. A single natural‑language query triggers the entire diagnostic pipeline, producing a concise fault report and actionable recommendations.

Use Case 2: Fault Progression

AI agents proactively engage with vendors, track progress, and close loops without human intervention, turning passive alarm handling into active, autonomous resolution.

Use Case 3: Customer‑Specific Visual Dashboards

During Double 11, Baidu delivered AI‑generated, on‑demand network protection dashboards for cloud customers, achieving instant, customized visualizations without manual development.

Quantitative Impact

In November 2025, AI model calls reached 174,900 (a 486.9 % month‑over‑month increase). Compared to traditional manual processes, AI reduces per‑task cost to ¥0.65 (vs ¥16) and execution time to 30 seconds (vs 8 minutes), delivering a 23‑fold ROI and 16‑fold speedup. Accuracy exceeds 95 %.

Future Roadmap

By 2028, Baidu aims to evolve from highly automated to fully autonomous, self‑optimizing networks with capabilities such as fault prediction, digital twins, and zero‑touch change management. The three‑year plan outlines milestones for 2026 (high automation), 2027 (intelligent services), and 2028 (cognitive decision‑making).

Vision

AI will become engineers’ “super‑assistant,” shifting operations from labor‑intensive to intelligence‑centric, enabling proactive risk mitigation and cross‑industry empowerment.

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