TL;DR
- MCP and A2A are open protocols making AI integration scalable, secure, and platform-independent.
- Centralized and hybrid “hub-and-spoke” architectures support scalable, federated AI adoption.
- MCP provides the backbone for connecting AI agents with tools, data, and reusable prompts.
- A2A is an open standard for seamless communication and collaboration between agents from different vendors.
Introduction
The AI world is rapidly evolving, and the emergence of agentic ecosystems is at the center of this transformation. In our latest webinar, ableneo’s Štefan Grivalsky guided participants through the foundational protocols powering this shift. The session detailed how open standards like MCP and A2A, initially adopted by industry leaders such as Anthropic, OpenAI, and Google DeepMind, are now essential for building business-ready, interoperable AI. Attendees saw how these protocols turn complexity into opportunity and enable organizations to innovate confidently.
Why Protocols Matter in AI
Protocols are standardized rules that make integration easy and platform-independent. In the agentic ecosystem, protocols are not barriers. They are enablers of interoperability.
- Model Context Protocol (MCP):
This open-source standard connects AI applications to external systems. First introduced by Anthropic in November 2024, MCP was soon adopted by OpenAI and Google DeepMind. MCP lets AI agents access tools (APIs, functions), resources (data entities such as files or schemas), and prompts (structured templates for LLM conversation or behavior).
- Agent2Agent (A2A):
Developed by Google and donated to the Linux Foundation, A2A enables seamless, secure, and auditable collaboration between AI agents built using diverse frameworks and by different vendors. It provides a universal handshake for agentic interaction, including agent discovery, authentication, and secure communication.
Key Features and Practical Lessons
- MCP in Action:
MCP organizes agent abilities into three categories: Tools (functions/APIs), Resources (data for context), and Prompts (reusable message templates). Agents use MCP to discover what tools are available, request actions, and coordinate responses. This foundational layer is crucial for business process automation and optimization.
- A2A for Multi-Agent Collaboration:
A2A allows agents to exchange information securely using identity verification, HTTPS, and TLS. Agents present “Agent Cards” with identity, skills, and service endpoints, supporting both direct and catalog-based discovery. A2A is not an orchestration system but a protocol for secure, auditable agent collaboration.
- Hybrid “Hub-and-Spoke” Architectures:
The agentic ecosystem can be deployed in centralized, decentralized, or hybrid (federated) ways. The recommended approach is hybrid: centralize the foundation layer (infrastructure, policy, registry), while decentralizing AI innovation across business domains (“hub-and-spoke” pattern).
Technical Impact
- Business Value:
MCP and A2A make it possible to integrate AI agents into real business workflows, not just for chat assistants but for complex optimization tasks. They support interoperability, scalability, and security, providing a foundation for future AI-driven transformation.
- Security and Compliance:
Both protocols are designed with security in mind, including HTTPS, TLS, and identity verification, ensuring trust and traceability for enterprise deployments.
What’s Next?
The agentic ecosystem is now a practical reality. By adopting open standards like MCP and A2A, organizations can build flexible, scalable, and secure AI solutions that drive real business value.
Want More?
Access our webinar recoding or get in touch with us.
Ableneo Office
Bratislava
+421 2 32 144 791
Info-sk@ableneo.com
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Author
Štefan Grivalsky
Senior AI Engineer, ableneo
Focused on helping organizations create real business value from AI
