I. Intelligent Agent Module: Full-Scope O&M Automation Loop
Focusing on key IT operations and maintenance (O&M) scenarios, the system builds seven specialized intelligent agents, achieving end-to-end intelligent automation across the entire O&M lifecycle.
1. Knowledge Agent — Full-Lifecycle O&M Knowledge Management
· Knowledge Ingestion & Preprocessing: Develops automated tools for document parsing and bulk import through system integration, with manual review support.
· Thematic Knowledge Base Construction: Builds scenario-based knowledge bases (e.g., change review, emergency plans) and synchronizes vendor product knowledge.
· Knowledge Accuracy Optimization: Integrates OCR, knowledge graphs, FAISS vector databases, and RAG technology to automatically verify and refine knowledge accuracy.
· Knowledge Operation & Lifecycle Tracking: Supports version control and full-process traceability, logging knowledge creation, review, and updates.
2. Change Management Agent — Intelligent ITSM Upgrade
· Change Compliance Pre-Review: Integrates with knowledge bases and ITSM data to automatically validate change compliance via AI.
· Approval Brief Generation: Extracts structured key data and generates concise approval summaries with one-click Word export.
· Change Summary Dashboard: Categorizes and aggregates changes by type, automatically generating summaries by count, risk level, and other metrics.
· Change Closure & Archival: After ticket closure, automatically produces post-change summary reports and archives them in the knowledge base.
3. Inspection Agent — Automated, Scenario-Based Inspections
· Inspection Requirement Recognition: Through natural language dialogue, identifies O&M roles and contexts to generate inspection workflows.
· Tool Scheduling & Execution: Integrates MCP server, SSH, and existing platforms for fully automated inspection execution.
· Inspection Report Generation: Leverages large models (e.g., Qwen 2.5:32B) to generate reports summarizing exceptions and recommendations.
4. Fault Analysis Agent — Intelligent Observability & Diagnostics
· Intelligent Observability Scenarios: Enables automatic fault localization and auto-matching of emergency response playbooks.
· Operational Data Modeling: Incorporates multiple O&M data types, builds architecture-specific data models, and aggregates key incident data.
· Data Governance: Enhances metric and log collection, enriches metadata, and improves overall data quality.
5. Data Query Agent — Intelligent Data Interaction (“Ask-Data”)
· Natural Language Query: Converts natural language into structured queries for bi-directional data interaction.
· Instant Report Generation: Produces data tables and visualizations in minutes, reducing report turnaround time from days to minutes.
· Advanced Statistical Analysis: Supports multi-dimensional and domain-specific analytics, enabling data-driven O&M decisions.
6. Capacity Analysis Agent — Intelligent Resource Management
· Resource Demand Forecasting: Uses historical and observability data to predict resource utilization under different workloads.
· Resource Correlation Analysis: Identifies dependencies between resource usage, business indicators, and system events.
· Optimization Strategy Generation: Automatically formulates optimal resource allocation plans aligned with business objectives.
7. Report Generation Agent — Automated O&M Report Production
· Multi-Report Support: Automatically generates change reviews, weekly/monthly/quarterly summaries, and fault analysis reports.
· Automated Workflow: Integrates with O&M data and documentation, supporting Markdown-to-Word conversion and online editing.
· Standardization & Personalization: Applies standardized templates while tailoring content detail based on user roles.
II. Foundational Technical Platform — The Backbone of Intelligent O&M
The system establishes a robust technical foundation across models, AI tools, and data infrastructure to ensure reliable O&M automation.
1. Large Model Foundation
· Model Selection & Adaptation: Integrates domain-specific (e.g., Baichuan-13B) and general-purpose (e.g., Qwen) LLMs, supporting deployment on domestic Haiguang DCUs.
· Model Optimization: Enhances model outputs through Prompt Engineering, fine-tuning embedding and base models for O&M-specific contexts.
2. AI Platform Support
· Integrated AI Toolchain: Incorporates Coze, Qwen-Agent, and other orchestration tools to define agent roles and operational constraints.
· Tool Invocation Capabilities: Builds a Tool List (e.g., CMDB query, SQL generation/optimization tools) to enable seamless interaction with existing O&M systems.
3. Data and Vector Database Support
· Vector Database Deployment & Management: Deploys a FAISS vector database with metadata filtering for high-precision retrieval.
· Data Service Enablement: Establishes log, metric, and tracing data services integrated with GitOps and Wiki systems.
III. Security and Compliance Module — Safeguarding Operations and Data
Ensures both data-level and operational security through a multi-layered control framework.
· Data Access Security: Implements role-based access control (RBAC) to restrict data query and modification privileges, preventing sensitive data leakage.
· Operational Security: Logs and audits all intelligent-agent automation activities, supports operation rollback, and reduces the risk of misoperation.
