Contributed by Jesse Saffran
At the DataOps Initiative, we’re fortunate to have some brilliant folks who enjoy sharing their expertise and knowledge. Together we explore topics in open discussions amongst the group to better understand different use cases and theory. We recently held a discussion of Model Context Protocol (MCP) and Agent-to-Agent Protocol (A2A) led by Vivek Mangipudi. You can follow along with the comparison spreadsheet Vivek created for additional information.
What is MCP?
MCP is used as a sort of base foundational layer through preparation of the correct context and resources to provide AI agents with the correct data and tools while ensuring accuracy and auditability.
MCP primarily focuses on structured data (JSON, files, images, database rows) with the added security benefit of all interactions going through managed services that enable full logging and monitoring of calls.
In a DataOps context, MCP would be used to integrate your raw data (data lakes, Kafka streams, pipeline metadata) into the AI driven data pipeline without the need to hand code API access for every data source.
What is A2A?
A2A provides a standardized means of communication between autonomous agents. This protocol enables agents to quickly discover and collaborate together on tasks. Due to the multi-modal support, agents can determine the best collaboration format. Agent interactions are made easier through peer discovery at runtime via the AgentCard structure through which agents advertise their name, skills, and authentication scheme.
A2A also provides robust multi-point logging capabilities at agent endpoints or on the event bus. Multi-agent workflow can be easily traced via task states or artifacts. Additionally, Kafka-based patterns can provide replayable logs of all agent exchanges.
How do they work together?
To steal a wonderful analogy from our discussions on the topic; Think of MCP as a lake, and agents of A2A as birds. In this way MCP provides the water, fish, plants (data/tools) that birds (agents) swoop in and grab as needed for their various tasks.
A layered system architecture might look like:
Stacking MCP and A2A enables us to build secure, contextual, and composable agent-based systems and to allow data flows and AI tasks that can collaborate across teams and tools.
View the full discussion on our YouTube channel.
Are you interested in learning more or sharing in our discussions? Join the DataOps Initiative.
