peterindia.netAI Agent Meshes and Interoperability
Protocols, meshes, gateways and platforms that let AI agents discover each other, call tools and work together across vendors and frameworks: A2A, MCP, agent meshes, agent gateways and enterprise agent platforms.
An open standard for communication between AI agents built with different frameworks, originally developed by Google and now run under the Linux Foundation.
Best for: Agents delegating work to other agents.
Visit A2A (Agent2Agent) Protocol →An open-source standard for connecting AI applications to data sources, tools and workflows.
Best for: Giving agents access to tools and data.
Visit Model Context Protocol (MCP) →An open-source stack for an internet of agents, with discovery, secure communication (SLIM), identity, runtime and observability. Governed by a steering committee that includes Cisco, Dell, Google, Oracle and Red Hat.
Best for: Cross-vendor agent discovery and identity.
Visit AGNTCY →A REST-based agent interoperability protocol developed with BeeAI. Its documentation says it is now part of A2A under the Linux Foundation.
Best for: Understanding the A2A lineage.
Visit Agent Communication Protocol (ACP) →An open protocol for connecting AI agents to user-facing applications, covering streaming events and shared state.
Best for: Agent-to-user-interface connections.
Visit AG-UI →A platform for building, deploying and observing agents, with support for open protocols including A2A and MCP. Free to evaluate, and free for organisations under US$25 million in revenue.
Best for: Event-driven agent meshes.
Visit Solace Agent Mesh →An open-source gateway for HTTP and gRPC traffic and AI-native protocols, including LLM routing, MCP and A2A. Hosted by the Linux Foundation through the Agentic AI Foundation.
Best for: One data plane for apps and agents.
Visit agentgateway →Kong's gateway for governing LLM, MCP and A2A traffic, with authentication, observability and PII sanitisation.
Best for: Extending an API gateway to agents.
Visit Kong AI Gateway →A gateway between agents and MCP servers that controls tool access with centralised identity, policy and credentials, and records tool calls for audit.
Best for: Governing which tools agents may call.
Visit Docker MCP Enterprise Gateway →An agent management platform to build, orchestrate and govern agents in one control plane, with an agent and tool catalog and support for A2A and MCP.
Best for: Central agent catalog and governance.
Visit IBM watsonx Orchestrate →A platform to build, govern and orchestrate agents, with automatic cataloguing of agents from multiple providers, monitoring and MCP tool support.
Best for: Agent management alongside integration.
Visit Boomi Agentstudio →AWS's platform for deploying and operating agents built with any framework or model, with a runtime, a gateway for tool access and observability.
Best for: Running agents securely on AWS.
Visit Amazon Bedrock AgentCore →Microsoft's framework for building agents and workflows in Python and C#, with migration guides from AutoGen and Semantic Kernel.
Best for: Microsoft-based agent development.
Visit Microsoft Agent Framework →An open-source agent framework available in Python, TypeScript, Go, Java and Kotlin, with built-in support for A2A and MCP tools.
Best for: Building A2A-capable agents.
Visit Agent Development Kit (ADK) →An SDK for stateful agents with persistent memory and a global runtime, with support for remote MCP servers.
Best for: Edge-hosted agents.
Visit Cloudflare Agents SDK →Match your requirement to a starting point, then compare vendors.
| Agent needs tools and data | MCP servers behind a gateway with access control |
|---|---|
| Agents must call other agents | A2A, ideally with registry and identity services |
| Many agents across teams | An agent platform with a catalog, policy and observability |
| Event-driven or real-time agents | An agent mesh built on an event broker |
| Already have an API gateway | Check whether it extends to MCP and A2A traffic |
| Building new agents | A framework with built-in A2A and MCP support |
Neighbouring directories and hubs.
Quick answers.
An agent mesh is an architecture that connects many AI agents, tools and services so they can discover each other and exchange tasks through shared protocols, with common routing, security and observability, much as a service mesh does for microservices.
MCP standardises how an agent connects to tools and data. A2A standardises how agents talk to other agents. They are complementary, and many platforms support both.
An agent gateway sits between agents, models and tools and enforces authentication, policy, rate limits and logging for protocols such as MCP and A2A, as an API gateway does for APIs.
Probably not for one or two agents. A mesh or platform becomes useful when many agents from different teams or vendors need to find and call each other and be governed consistently.
Give agents their own identities, limit each agent to the tools it needs, route calls through a gateway that logs them, and review third-party agents and MCP servers before connecting them.
They are young and changing. Check the current specification versions and governance before committing, and prefer products that support more than one protocol.
Descriptions are based on each project's or vendor's own site, checked on 6 October 2026. Agent standards and products are changing quickly, so confirm current versions and governance before deciding.