Open-Standard Protocols for AI Integration

I’ve been diving into new ways to streamline the development of agentic AI products, and some emerging standards are quickly becoming foundational. Here are three protocols that are shaping the ecosystem: 🔹 MCP (Model Context Protocol) – Developed by Anthropic, MCP is a single-agent protocol that connects agents to APIs, prompts, tools, and data sources. It acts as the integration layer that powers tool-augmented reasoning. 🔹 ACP (Agent Communication Protocol) – From IBM, ACP enables multi-agent orchestration using REST and async communication. It’s built on top of MCP, allowing agents to collaborate effectively under a centralized controller. 🔹 A2A (Agent-to-Agent Protocol) – Google’s latest contribution, A2A (2025), enables fully decentralized agent collaboration via async, RPC-style messaging. Each agent maintains its own session and state — ideal for building autonomous, distributed systems. 💡 The takeaway: MCP gives your agents tool access, ACP lets them coordinate, and A2A takes that coordination to a new level with autonomy and scale. If you’re building next-gen agentic systems, it’s worth exploring these protocols in depth. I’ve included resources and diagrams to help you get started (comment section below) 👇🏻 #AI #AgenticAI #MCP #ACP #A2A #LLM #MultiAgentSystems #ArtificialIntelligence #Developers #TechTrends2025

𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗔𝗴𝗲𝗻𝘁 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆 𝗣𝗿𝗼𝗽𝗼𝘀𝗮𝗹 𝗳𝗼𝗿 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 The proliferation of specialized AI agents presents a new challenge for enterprises: how to manage a diverse ecosystem of internal and external agents. The A2A (Agent-to-Agent) protocol, now under the stewardship of the Linux Foundation, is a foundational step towards interoperability. A new proposal on the A2A GitHub extends this with the concept of an Agent Registry. This registry aims to provide a framework for the discovery, governance, and entitlement of AI agents. The core components include:  • An Agent Catalog: To list and categorize available agents.  • Standardized Agent Cards: To provide machine-readable details of agent capabilities.  • A System for Agent Entitlements: To ensure secure and controlled access. This proposal moves beyond simple API documentation and towards a more dynamic and manageable multi-agent architecture. This raises some important questions for the community:  1. To what extent can a standardized registry reduce the friction of integrating new agents into complex enterprise workflows?  2. How can we best align the proposed entitlement model with existing Zero Trust security frameworks and corporate IAM solutions?  3. Beyond discovery and entitlements, what mechanisms for reputation and trust scoring should be considered for agents in a shared ecosystem? I encourage you to read the full proposal and contribute to this important conversation. Full proposal: https://lnkd.in/grFT4t6C #AI #AgenticAI #A2A #MultiAgentSystems #Interoperability #OpenStandards #EnterpriseArchitecture #Google #LinuxFoundation

AI Agent Protocols: A Side-by-Side Comparison You Need to Know As AI agents evolve from simple tools to collaborative, networked systems, the protocols they use to communicate become critical. Here’s a clear breakdown of 4 major Agent Communication Protocols: 🔹 MCP (Model Context Protocol) – Developed by Anthropic 🧱 Architecture: Client-Server 🔐 Session: Stateless 🌐 Discovery: Manual Registration 🚀 Strength: Best for tool calling ⚠️ Limitation: Limited to tool interactions 🔸 A2A (Agent to Agent Protocol) – Developed by Google 🧱 Architecture: Centralized Peer-to-Peer 🔐 Session: Session-aware or stateless 🌐 Discovery: Agent card retrieval via HTTP 🚀 Strength: Great for inter-agent negotiation ⚠️ Limitation: Assumes presence of agent catalog 🔷 ANP (Agent Network Protocol) – Developed by Cisco 🧱 Architecture: Decentralized Peer-to-Peer 🔐 Session: Stateless with DID authentication 🌐 Discovery: Search engine-based 🚀 Strength: Built for AI-native negotiation ⚠️ Limitation: High negotiation overhead 🟦 ACP (Agent Communication Protocol) – Developed by IBM 🧱 Architecture: Brokered Client-Server 🔐 Session: Fully session-aware with run-state tracking 🌐 Discovery: Registry-based 🚀 Strength: Modular and extensible ⚠️ Limitation: Requires registry setup 💡 Each protocol serves a different use case — from tool integration to peer-to-peer negotiation and registry-based modular systems. The choice depends on your architecture, goals, and how dynamic your agents need to be. Are you building AI agents that need to collaborate or scale across networks? Understanding these protocols could be your next big unlock.