Deeply researching next-generation interoperability protocols: Model Context Protocol (MCP), Proxy Communication Protocol (ACP), Proxy-to-Proxy Protocol (A2A), and Proxy Network Protocol (ANP)
As autonomous systems increasingly rely on large language models (LLMs) for reasoning, planning, and action execution, key bottlenecks emerge, not capabilities, but in communication. Although LLM proxies can parse descriptions and call tools, their ability to interoperate with each other in a scalable, secure and modular way remains deeply bounded. Vendor-specific API, ad hoc integration and static tools registry silo existing systems. To break this cycle, four emerging protocols, Model Context Protocol (MCP), Proxy Communication Protocol (ACP), Proxy-to-Proxy Protocol (A2A), and Proxy Network Protocol (ANP) provide a roadmap to standardize interoperability across proxy infrastructures.
Called using Model Context Protocol (MCP) Standardization Tool
LLM proxy is context-dependent in nature. They need structured, precise input patterns to generate SQL queries, retrieve documents, or call APIs. Historically, this background was embedded in hints or hardcoded logic, but this approach is both fragile and worthless. MCP reconceives this interface by defining a JSON-RPC-based mechanism through which tool metadata and structured context can be ingested. MCP acts as an interface layer between the proxy and its external functions. It allows developers to dynamically register tool definitions, including parameter types, expected outputs, and usage constraints, and expose them to the proxy in a standardized format. This enables real-time verification, secure execution and seamless tool replacement without proxy retraining or timely rewrites. MCP enables modular and infrastructure-unstable integration through “USB-C” used as an AI tool. It also supports vendor neutrality, allowing agents to use the same context interface on LLMs of different providers, which is critical for enterprise adoption.
Asynchronous messaging and observability in ACP
When multiple agents run in a local environment, such as in a shared container or enterprise application, they require a way to communicate effectively. Agent Communications Protocol (ACP) is designed to meet this need. Unlike traditional RPC interfaces, ACP introduces a rest-local, asynchronous messaging layer that supports multimodal content, real-time updates and tolerate mistakes. ACP allows the proxy to send multi-part messages, including structured data, binary BLOBs, and context descriptions. It supports stream response, allowing the agent to provide incremental updates during task execution. ACP is SDK-AGNOSTIC and adheres to the open standards, allowing implementation in any language and seamless integration into existing HTTP-based systems. Another core feature of ACP is observability. ACP-compatible agents can record communications through built-in diagnostic hooks, reveal performance metrics, and track errors between distributed tasks. This is crucial in a production environment where debugger behavior is opaque.
Peer collaboration through proxy-to-proxy protocol (A2A)
Agents often require collaboration across domains, organizations, or cloud environments. Static APIs and shared memory models do not resolve the dynamic and secure coordination required for such workflows. The Agent-to-Proxy Protocol (A2A) introduces a peer-to-peer communication framework built around capability-based delegates. At the heart of A2A are proxy cards, independent JSON descriptors, advertising agent capabilities, communication endpoints and access policies. These cards are exchanged during the proxy handshake, allowing two autonomous entities to negotiate the terms of collaboration before performing any tasks. A2A is impossible to transmit, but is often implemented through HTTP and server-volume events (SSE), enabling low latency, push-based coordination. It performs well in solutions such as enterprise automation, where different department agents can manage documents, schedules, or analysis, but must be coordinated without revealing internal logic or undermining security.
The benefits of A2A include:
- Modular delegation of tasks between peers with well-defined functional scope
- Ensure negotiation of resource access and execution conditions
- Real-time, event-driven updates through light messaging mode
This architecture allows agents to form distributed workflows without central orchestration, enabling organic task allocation and autonomous decision-making.
Open WEB coordination using Proxy Network Protocol (ANP)
Identity authentication and trust management are found to be the most important thing for agents running on the open internet. Proxy Network Protocol (ANP) provides the basis for decentralized proxy collaboration by combining semantic web technologies with encrypted identity models. ANP uses W3C-compliant Decentralized Identifiers (DIDS) and JSON-LD graphs to create self-described, verifiable proxy identities. Agents publish metadata, ontology, and capability graphs that allow other agents to discover and interpret their products without a centralized registry. Security and privacy are indispensable to ANP. It supports encrypted message channels, requested encrypted signatures, and selective disclosure of proxy functions. These features enable agency markets, joint research networks, and trustless collaboration across borders or organizations. Through its semantic context and decentralized identities, ANP brings DNS and TLS to the early Internet, discoverability, trust, and security.
The evolution of interoperability: from static APIs to dynamic protocols
Interoperability work in proxy systems dates back to the 1990s and has symbolic languages such as KQML and FIPA-ACL. These early attempts established a model of the mental state of form of performance structures and promoters, but suffered from detailed symptoms, a lack of dynamic discovery, and an over-reliance on XML. The 2000s saw an increase in service-oriented architecture (SOA), where agents and services interacted through SOAP and WSDL. Although modular in principle, these systems introduce configuration sprawl, tight coupling and low change adaptability. However, modern LLM proxy requires a new paradigm. Innovations such as call and retrieval generation allow model reasoning and act in a unified workflow. However, these models remain isolated without dynamic capability exchange, cross-agent negotiation, or sharing mode. Current protocols, MCP, ACP, A2A and ANP represent a transition from a static, closed system to an adaptive, open ecosystem.
Roadmap to a scalable multi-agent system
The structure of interoperability is not holistic. Each protocol addresses different levels of proxy collaboration, and together they form a coherent deployment roadmap:
- MCP enables structured, secure access tools and datasets
- ACP introduces asynchronous, multi-mode proxy messaging
- A2A allows secure peer-to-peer capabilities negotiation and authorization
- ANP supports open WEB proxy discovery and decentralized identities
This stratification strategy allows developers and enterprises to integrate and scale from local to fully decentralized autonomous proxy networks, thereby gradually adopting capabilities.
In short, these protocols are not only communication tools, but also the building foundation for the next generation of autonomous systems. As AI agents spread across the cloud, edge and enterprise environments, the ability to firmly, modularly and dynamically interoperate becomes the foundation of smart infrastructure. Through shared patterns, open governance and scalable security models, these protocols enable developers to go beyond custom integration and common proxy interface standards. Just like HTTP and TCP/IP, the foundations of MCP, ACP, A2A and ANP are the foundations of the AI-Native software ecosystem.
Sana Hassan, a consulting intern at Marktechpost and a dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. He is very interested in solving practical problems, and he brings a new perspective to the intersection of AI and real-life solutions.
