Selecting agentic protocols
For most organizations building production agent systems, the Model Context Protocol (MCP) offers the most comprehensive and well-supported foundation for agent-to-agent communication. MCP benefits from active development contributions from AWS and the open-source community.
Selecting the right agentic protocols is important for organizations looking to implement agentic AI effectively. Considerations differ based on organizational context.
Agentic protocol selection considerations
Organizations should consider the following best practices when selecting protocols for agentic AI systems::
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Prioritize open standards – Organizations should adopt open protocols such as the MCP to help ensure long-term interoperability, extensibility, and to reduce the risk of vendor lock-in.
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Balance speed and flexibility – Startups and early adopters may begin with well-supported proprietary protocols for rapid development but should define a migration path to open standards as systems mature.
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Implement abstraction layers – Enterprises should implement protocol abstraction to simplify migration, enable hybrid adoption, and future-proof integration strategies.
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Emphasize security and compliance – Organizations in regulated industries should select protocols with robust authentication, encryption, and audit capabilities to meet governance and compliance requirements.
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Evaluate ecosystem maturity – All organizations should assess the health, adoption, and community support of each protocol to ensure sustainability and minimize technical debt.
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Engage in standards development – Organizations should participate in standards bodies or open-source communities to help shape protocol evolution and influence best practices.
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Account for data sovereignty – Government and regulated sectors should ensure protocol choices align with data residency and sovereignty requirements across deployment regions.
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Leverage managed services – Where possible, use managed or serverless implementations of agentic protocols to reduce operational complexity and accelerate deployment.
