An AI agent protocol is a standardized set of rules, message formats, and interaction patterns that define how AI agents communicate with other agents, tools, services, or data sources. These protocols ensure interoperability, security, and predictable behavior across diverse agent ecosystems.
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An AI agent protocol may cover:
- Message exchange formats (e.g., JSON-RPC, REST, DID-based messaging)
- Discovery mechanisms to find other agents or resources
- Negotiation processes for capabilities, permissions, and tasks
- Security requirements such as authentication, encryption, and access control
- Governance and auditability features to meet compliance or policy requirements
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Example:
Using the Agent-to-Agent (A2A) protocol, two AI agents from different departments can securely negotiate and share data without custom APIs.
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Related terms:
- Model Context Protocol (MCP) – A popular AI agent protocol for tool and data integration.
- Agent-to-Agent Protocol (A2A) – An open standard that enables AI agents from different systems to securely communicate, delegate tasks, and exchange results in a consistent, interoperable way.
- AI Agentic Framework – A software toolkit or runtime environment for building, running, and orchestrating AI agents with capabilities like planning, tool use, memory, and multi-agent coordination.
