Grace Is a Leadership Strategy — Here’s How CEOs Can Use It Effectively

A growing number of companies are launching AI agents that can do things on your behalf—actions like sending an email, making a document, or editing a database. Initial reviews for these agents have been mixed at best, though, because they struggle to interact with all the different components of our digital lives.

Part of the problem is that we are still building the necessary infrastructure to help agents navigate the world. If we want agents to complete tasks for us, we need to give them the necessary tools while also making sure they use that power responsibly.

Anthropic and Google are among the companies and groups working to do those. Over the past year, they have both introduced protocols that try to define how AI agents should interact with each other and the world around them. These protocols could make it easier for agents to control other programs like email clients and note-taking apps.

The reason has to do with application programming interfaces, the connections between computers or programs that govern much of our online world. APIs currently reply to “pings” with standardized information. But AI models aren’t made to work exactly the same every time. The very randomness that helps them come across as conversational and expressive also makes it difficult for them to both call an API and understand the response.

“Models speak a natural language,” says Theo Chu, a project manager at Anthropic. “For [a model] to get context and do something with that context, there is a translation layer that has to happen for it to make sense to the model.” Chu works on one such translation technique, the Model Context Protocol (MCP), which Anthropic introduced at the end of last year.

Working out how to govern how AI agents interact with each other is arguably an even steeper challenge, and it’s one the Agent2Agent protocol (A2A), introduced by Google in April, tries to take on. Whereas he Model Context Protocol (MCP) translates requests between words and code, A2A tries to moderate exchanges between agents, which is an “essential next step for the industry to move beyond single-purpose agents.

Google says 150 companies have already partnered with it to develop and adopt A2A, including Adobe and Salesforce. At a high level, both MCP and A2A tell an AI agent what it absolutely needs to do, what it should do, and what it should not do to ensure a safe interaction with other services. In a way, they are complementary—each agent in an A2A interaction could individually be using MCP to fetch information the other asks for. 

However, Chu stresses that it is “definitely still early days” for MCP, and the A2A road map lists plenty of tasks still to be done. We’ve identified the three main areas of growth for MCP, A2A, and other agent protocols: security, openness, and efficiency.

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