Integrate both task lists and event lists in agent interfaces to provide visibility into pending actions and historical events, forming the core capabilities of AI agents.
The A2A agent card schema defines metadata, source server, and tool listings to enable agents to understand and interact dynamically with third-party agents.
A custom MCP integration differs from the standard tooling diagram by embedding context mechanisms deeper in your application stack for specialized workflows.
Agent architectures built on the A2A protocol allow you to mix and match frameworks, cloud providers, and multiple LLMs, enabling a fully customized tech stack.
The A2A protocol diagram illustrates how to integrate this communication mechanism into agentic applications to standardize interactions between components.
You can register LangGraph agents as tools within any MCP client, firing off queries to remote agents for specialized tasks like deep research or coding.
The A2A agent-to-agent protocol can be used alongside MCP to standardize I/O formats and enable seamless communication between agents built on different frameworks.
Structure AI systems with two stacks: one set of specialized tools for direct vending-machine interactions and another for remote business-planning tools like email and web search.
