Build unified intelligence with Amazon Bedrock AgentCore

2026-02-18 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Advanced, long

Summary

AWS has developed the Customer Agent & Knowledge Engine (CAKE), a customer-centric chat agent built using Amazon Bedrock AgentCore, to unify fragmented customer intelligence for its global sales organization. CAKE integrates data from various specialized sources, including knowledge graphs in Amazon Neptune, metrics in Amazon DynamoDB, documents in Amazon OpenSearch Service, and external market data via a web search API. This multi-agent system coordinates these tools through dynamic intent analysis and parallel execution, delivering personalized customer insights via natural language queries in under 10 seconds. It also incorporates row-level security (RLS) and production engineering practices for reliability and performance optimization, addressing challenges like fragmented data, loss of business semantics, and manual consolidation processes.

Key takeaway

For AI Architects and Machine Learning Engineers building enterprise intelligence systems, Amazon Bedrock AgentCore offers a managed service to accelerate multi-agent AI development. You should consider leveraging its orchestration capabilities to integrate diverse data stores and implement robust security, reducing infrastructure overhead and focusing on business logic to deliver unified, context-rich insights quickly.

Key insights

Amazon Bedrock AgentCore enables unified customer intelligence through multi-agent orchestration of diverse data sources.

Principles

• Unify fragmented data for comprehensive customer views.
• Preserve business semantics with knowledge graphs.
• Implement row-level security at the data layer.

Method

A supervisor agent analyzes intent, dispatches specialized retriever tools in parallel (e.g., Neptune, DynamoDB, OpenSearch), synthesizes results, and enforces security via an RLS tool, all orchestrated by Amazon Bedrock AgentCore.

In practice

• Use Neptune for graph relationship queries.
• Employ DynamoDB for instant metric lookups.
• Utilize OpenSearch Service for semantic document search.

Topics

• Amazon Bedrock AgentCore
• Multi-agent Systems
• Customer Intelligence
• Knowledge Graphs
• Row-Level Security

Best for: Machine Learning Engineer, AI Architect, Data Engineer

Related on AIssential

• Powering agentic AI sales strategy with Amazon Bedrock AgentCore — Orchestrating specialized AI agents with a unified interface significantly reduces cognitive load and improves operational efficiency.
• AWS Bedrock Agents: The Beginner’s Guide — AWS Bedrock Agents provide a managed platform for building and deploying AI agents with integrated orchestration and security.
• Build AI agents for business intelligence with Amazon Bedrock AgentCore — AI agents on Amazon Bedrock AgentCore can autonomously process fragmented business data for real-time BI.
• Build and Run Your Own AI Agent in the Cloud — AgentCore provides managed AWS services for deploying and operating framework-agnostic AI agents, complementing agent frameworks like Strands.
• Amazon’s OpenAI gambit signals a new phase in the cloud wars — one where exclusivity no longer applies — AWS's new offerings position it as a comprehensive platform for agentic AI, integrating top models with robust security and specialized applications.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.