Agent interoperability: a complete explainer

Agent interoperability refers to the ability of AI agents built on different frameworks to discover, collaborate, and share context without requiring custom integration code, which is increasingly important as multi-agent systems become mainstream. The adoption of standard protocols, such as the Model Context Protocol (MCP) for agent-to-tool interactions and the Agent2Agent (A2A) Protocol for inter-agent communication, is crucial for overcoming integration challenges and reducing duplicated efforts. However, these protocols do not address the issue of shared state, which is essential for maintaining coherent agent contexts across different frameworks and tasks. Redis Iris is presented as a solution for this challenge, providing a real-time data platform that supports shared memory, vector search, semantic caching, and pub/sub coordination, enabling agents from various frameworks to operate from a unified state layer. This shared-state layer helps prevent context fragmentation and enhances the capability of agents to work collaboratively, offering significant cost efficiencies and improved accuracy in AI applications.

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