Generate Autonomous Business Insights with AI Agent and MCP Servers
Amazon Web Services
Amazon Bedrock AgentCore enables autonomous business insights by orchestrating AI agents across enterprise data sources using MCP servers, eliminating manual data stitching. The architecture connects data systems, enforces access policies, and allows natural language queries for real-time, personalized answers.
The article presents a solution for generating autonomous business insights using AI agents and MCP servers, built on Amazon Bedrock AgentCore. It illustrates a typical scenario where Sarah Chen, a plant manager, struggles to get cross-system answers by manually combining data from IoT dashboards, ERP systems, and historian databases. The proposed architecture comprises five layers: users, Amazon Bedrock AgentCore (orchestration), MCP servers (tool connectors), data infrastructure (SageMaker Lakehouse, Redshift, etc.), and policy enforcement. MCP servers expose typed tools for each domain (equipment, IoT, supply chain) and are either pre-built or custom. The Gateway provides unified routing, caching, and policy enforcement. The Semantic Layer, powered by SageMaker Data Catalog, helps the agent discover relevant data sources without hard-coded logic. The system is configuration-driven rather than code-heavy, enabling non-engineers to ask natural language questions and receive synthesized answers across all connected systems.
- Сокращения
- MCP = Model Context Protocol — Протокол контекста модели
- IoT = Internet of Things — Интернет вещей
- ERP = Enterprise Resource Planning — Планирование ресурсов предприятия
- OEE = Overall Equipment Effectiveness — Общая эффективность оборудования
- ETL = Extract, Transform, Load — Извлечение, преобразование, загрузка
- SaaS = Software as a Service — Программное обеспечение как услуга
- API = Application Programming Interface — Интерфейс программирования приложений
- CRM = Customer Relationship Management — Управление взаимоотношениями с клиентами
Source: AWS ML blog —
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