Build multi-agent teams that remember every customer with Amazon Bedrock AgentCore

说明与用途

使用 Amazon Bedrock AgentCore 和 n8n 构建记住每位客户的多代理团队。单个内存托管为四个专家服务，每个专家处理不同类型的查询，同时保留对话上下文。

问题

Customers need a support bot that can handle multiple types of queries (configuration, calculation, architecture, general) while preserving conversation context across agents without building separate vector stores or deploying multiple agents.

结果

The workflow demonstrates how to build a multi-agent support team with shared memory, reducing duplication and operational cost. It handles different query types, preserves context across agents, and returns answers in Slack with specialist tags.

工作流

• Connect AWS and Slack credentials in n8n
• Set Customer Context node to create Actor ID and Session ID
• Triage Agent classifies incoming question and returns category JSON
• Read Triage Decision parses the category
• Route To Specialist sends question to the matching specialist agent
• Each specialist agent runs with tools granted per call (Code Interpreter, AWS skills catalog, shell)
• Format Reply unwraps the answer
• Post Answer To Slack posts the answer tagged with the specialist

证据

AgentCore’s Managed memory is scoped by actor and session, so an Actor ID that identifies the customer rather than any single agent gives every specialist the same history to read and write

来源

The workflow creates one harness for all four agents rather than one each

来源