AWS 发布基于 Bedrock AgentCore 的企业级智能体平台架构
AWS 机器学习博客介绍了基于 Amazon Bedrock 与 Amazon Bedrock AgentCore 构建企业级智能体 AI 平台的架构与参考实现,涵盖多账户运行模式、集中管控平面、可重复的团队级 Workstream 单元、独立的推理网关与 MCP 工具网关,以及在智能体上线前进行门禁的评估流水线。文中称 AgentCore 提供运行时、网关、身份、记忆、策略、注册与评估等模块化服务,配套蓝图则把所有权、账户边界、软件交付、可观测性、成本治理与退役机制以基础设施即代码方式落地。文章说明部署与可用功能基于 eu-west-1(爱尔兰)区域,代码仓库为可自行调整的参考实现。
该架构为 AWS 上多团队智能体的统一治理提供了可落地参考,把账户隔离、工作负载身份、配额、回滚与遥测作为默认能力而非各团队自行拼装。
If you’ve tried to get an agent into production, the model and tool use are not the blocker. The main bottleneck is everything around them: how many teams can build within a secure environment, which team owns each model and tool, how AgentOps and error handling stay inside a pre-defined boundary, and how platform, Workstream, and finance teams see cost and health across accounts. For a Platform team that wants to support many agentic use cases, that’s a platform product to design and operate. In this post, we refer to the team that manages the platform as the Platform team, and to the teams that build on it as Workstream teams. Workstream teams are the domain or product teams that build and own agents on the platform.
Each Workstream team needs a paved road with approved models and tools, short-lived workload identity, isolated runtime and memory, automated evaluation, rollback, telemetry, and cost attribution. These controls need to remain consistent as Workstream teams customize prompts, tools, data access, and application behavior. When every team assembles them, controls drift, shared services become a bottleneck, and ownership is difficult to audit.
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