AWS has released a reference architecture for deploying AI agents that query data across multiple AWS accounts using Amazon Bedrock AgentCore Gateway and the Model Context Protocol (MCP). This solution enables enterprises to maintain data sovereignty within individual accounts while providing a unified endpoint for cross-account tool discovery and authorization.
This architecture addresses a critical friction point for large-scale streaming and media enterprises: the need for intelligent agents to access siloed data without the security risk of mass replication. By keeping data in its original account and only moving specific query results, AWS provides a scalable path for deploying RAG-based tools across complex organizational structures. This move strengthens the Amazon Bedrock ecosystem against competitors by offering a managed framework for governance and cost attribution at the account level. Industry observers should watch for the adoption of the Model Context Protocol as a standard for interoperability between specialized media microservices and central AI control planes.
The integration of FAST channel workflow orchestration within this gateway framework allows media companies to automate metadata injection and ad-insertion triggers across distributed cloud environments.
AWS has launched the AgentCore Gateway AI, a new architecture enabling AI agents to query data across distributed AWS accounts without centralizing datasets. By utilizing the Model Context Protocol, this system maintains data sovereignty while providing a unified endpoint, helping large-scale enterprises deploy secure, RAG-based tools across complex organizational structures.
The gateway acts as a central hub for tool discovery and invocation, allowing AI agents to query data across multiple AWS accounts without needing to move or centralize datasets.
Model Context Protocol servers run on the AgentCore Runtime to expose local data as structured tools, such as policy search or credit scoring, for use by AI agents.
Security is handled through AgentCore Identity and Okta using OAuth 2.0 credentials, while the Cedar language enforces fine-grained, per-user authorization at the gateway layer.
It allows intelligent agents to access siloed data without the security risks associated with mass replication, providing a scalable path for governance and cost attribution.
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