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Data and Analytics
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Artificial Intelligence
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HIGHLIGHTS
- As AI ecosystems evolve to become powerful, seamless integration with enterprise systems and other AI agents is crucial for realizing and achieving scalability. The need for standardised integration protocols is rising. Model Context Protocol (MCP) and Agent-to-Agent (A2A) are two emerging protocols offering required standardisation.
- A standard protocol that enables seamless integration between AI applications, enterprise tools, and data sources—while maintaining context—is essential for scalable use of enterprise data and services. MCP, developed by Anthropic, addresses this need and is backed by major LLM providers, enterprises, and the developer community.
- In multi-agent systems, standardized communication protocols are key to unlocking maximum value. A2A developed by Google in collaboration with a wide range of industry partners, provides a standard for communication between a "client" agent and a “remote” agent, regardless of the underlying framework or vendor.
On this page
AI ecosystems
AI applications are moving from simply assisting humans to ecosystems of purpose-built agents that can reason, plan, collaborate, and take action to complete tasks. AI models are becoming so capable that the challenge is now not the model capability but integration with enterprise ecosystems using existing or newly developed tools, data sources and other AI agents. Key issues enterprise face for integration and scaling AI ecosystems is as under:
- Custom implementation for each agent, data or tool
- Data and tools/ services in silos and isolated from AI systems
- Discovering the right agent, tool or service
- Communication between multiple agents from different providers
As interoperability standards evolve, approaches that are fast gaining ground to resolve these challenges are MCP and A2A.
MCP protocol
Despite the growing capabilities of AI systems, their isolation from enterprise data and tools remains a challenge. In November 2024, Anthropic released MCP. It is an open standard for AI applications to communicate with data, content repositories, tools, and dev environments and vice versa. MCP is backed by major LLM providers such as OpenAI and Google. It standardizes how tools and apps provide context to LLMs/AI applications.
MCP follows a client server architecture. MCP hosts are programs like Windsurf editor, Claude desktop or AI Assistants that need to access data or use services. MCP client connects hosts to MCP servers. MCP servers expose specific capabilities of tools, data and services using standard MCP protocol.
MCP: Communication between AI application and tool
MCP: Communication between AI application and tool
Benefits of MCP
- AI applications can integrate directly with tools that expose the MCP interface, eliminating the need to customize APIs for each tool.
- Developers can develop tools and services and make them compatible with the MCP protocol, and any AI app can discover and use them like a plug-and-play.
- AI applications can maintain context across different system interactions, more features and capability.
- Enterprise can have modular functionality of tools and services.
- Agents can access new capabilities updates from MCP server without any change of code.
- Mix and match with different AI providers and models seamlessly.
- MCP has garnered widespread developer adoption in a brief time. The developer community has built a substantial number of MCP integrations. As for Gartner-MCP is rapidly emerging as an AI integration standard.
A2A protocol
It is critical for AI Agents to collaborate with each other using standard protocol regardless of the underlying framework or vendor to realize maximum value. Google launched A2A protocol in April 2025 with support from 50+ partners including TCS, Salesforce, SAP and more. A2A provides standard for communication between a "client" agent and a “remote” agent and provides universal interoperability between agents.
A2A: Communication between AI agents
A2A: Communication between AI agents
A2A allows developers to make use of agents created by different providers. A2A protocol supports enterprise grade authentication and authorization providing security by default.
Conclusion
MCP is instruction-oriented. It uses structured schemas for communication like command with specific inputs, while A2A is goal-oriented and can take natural language for conversation like communication. MCP provides single stage task management. A2A can provide long running multistage task management. Requests like transactions, calculations with deterministic/ precise response are more suited for MCP. Requests which are more ambiguous, evolving requirements like research, communication with consultants can be suited for A2A.
A simple example can be a marketing campaign planning scenario where a central agent can plan and communicate with different specific purpose agents using A2A. Specific agents like product details agent, venue booking agent, ticketing agent, advertising agent, email agent, can use MCP to interact with respective resources.
Essentially using the right combination of protocol is important: Usage of MCP to access or execute specific tools/functions across the AI ecosystem and usage of A2A to plan and orchestrate complex workflows involving multiple agents is recommended.
