Practical applications of AI communication protocols (MCP, ACP, A2A, ANP)

Selecting a protocol is more than just a technical choice—it’s a strategic decision. The system’s architecture determines its scalability limits and ultimately determines what your AI infrastructure can and cannot achieve for the business.

In this article, we’ll break down the core capabilities, practical use cases, key challenges, and strategies to integrate protocols in your system.

Capabilities of AI communication protocols

AI communication protocols provide the core capabilities required to build a cohesive, scalable, and secure multi-agent system.

Interoperability and message standardization

This capability ensures that agents developed on different platforms can exchange information without misinterpretation. It is achieved by defining a common format and vocabulary for all communications.

Technical details

Across these protocols, JSON is standard: the Model Context Protocol (MCP) and Agent2Agent (A2A) use JSON‑RPC 2.0 message envelopes, while ACP exposes a REST+JSON surface. But JSON‑RPC is only one of A2A’s bindings. Protocols specify the exact structure of a message, for instance, requiring that a “task request” contain a task_id, description, and structured payload. This standardization ensures an agent built with one framework can correctly process a request from an agent built with another, preventing data corruption.

Orchestration and coordinated task management

Protocols provide mechanisms to manage complex workflows distributed across multiple autonomous agents, including task assignment, synchronization, and conflict resolution.

Technical details

This is implemented through a standardized task life cycle defined in protocols like A2A, which uses interrupting states—such as input-required and auth-required—as well as terminal states such as canceled or rejected. This framework enables advanced delegation, allowing agents to negotiate taking on tasks based on their advertised capabilities, typically through messages before a task is created.

Security, resilience, and trust

This capability ensures the agent network is robust, safe, and equipped for failures. It integrates security for data protection with resilience patterns for operational stability.

Technical details

• Security. Protocols enforce security through authentication and encryption. In enterprise systems, standards like Oauth and OpenID Connect (OIDC) establish identity trust, while decentralized systems use decentralized identifiers (DIDs) to enable trusted interactions. These measures ensure that only trusted agents can exchange data, preventing unauthorized access and preserving privacy in sensitive workflows. Beyond authentication, protocols often integrate transport-level encryption (e.g., Transport Layer Security (TLS)) and authenticated extended Agent Cards, using OAuth2/Bearer, mutual Transport Layer Security (mTLS), or JSON Web Token/JSON Web Key Set (JWT/JWKS) as specified in the Agent Card.
• Resilience. Protocols ensure resilience through retry logic, message queuing, and checkpointing to maintain workflow continuity. Frameworks like A2A add life cycle management and streaming updates to reliably handle multi-hour processes, while MCP’s Streamable HTTP transport supports resumability and redelivery using Server-Sent Events (SSE) Last-Event-ID. These mechanisms prevent data loss, incomplete tasks, and cascading failures, ensuring the system continues to function under stress.
• Trust. Protocols have embedded mechanisms for auditing, provenance tracking, and cryptographic signing of interactions. As a result, every agent action can be validated. Agent Network Protocol (ANP), for example, leverages DIDs and credentials for decentralized trust. Agent Communication Protocol (ACP) includes citations and trajectory features, while A2A relies on task metadata, SSE updates, and optional push-notify auth. By combining cryptographic assurance with transparent logs, trust frameworks help organizations meet compliance requirements, detect anomalies, and build confidence in agent-driven decisions.

Use cases and applications

The following AI communication protocol use cases showcase the role this technology plays in specific systems.

System integration

In enterprises, AI communication protocols operate at the data and workflow integration level. They enable large language model (LLM) integrations with internal APIs, enterprise agent interoperability, dynamic prompt construction, and multi-agent collaboration frameworks.

A global bank deploying an employee assistant must retrieve data from Salesforce, HR onboarding platforms, and internal knowledge bases simultaneously. Traditionally, each connection would require custom engineering. With MCP, these systems can be exposed to the assistant through a standardized interface, allowing the assistant to compose dynamic prompts and deliver unified answers.

Another example comes from enterprise sales and support teams, where autonomous agents operate across different departments to manage leads and handle customer inquiries. Without a shared communication framework, they risk duplicating operations or missing critical context. A2A solves this by enabling agents to share capabilities, exchange deal data, and coordinate task life cycles in real time, creating seamless data sharing across the organization.

Agent coordination

Building a system on different platforms is not an easy task. The standardized data formats used in AI communication protocols make it possible to create cross-platform agent ecosystems and support distributed agent orchestration.

A retail company may use Vertex AI for demand forecasting, HuggingFace for natural language analysis of customer feedback, and LangChain for chatbot orchestration—all with the aim of delivering a seamless customer experience. ACP, based on REST, enables fast integration and discovery across platforms, allowing businesses to collect customer insights and speed up decision-making.

In a multi-vendor marketplace, agents representing logistics providers, payment processors, and stores must coordinate checkout and delivery processes. Implementing A2A in these activities allows these agents to negotiate tasks across vendors, enabling courier reservations, payment processing, and inventory updates without relying on fragile custom integrations.

Environmental interaction

AI communication protocols allow developers to manage multi-agent orchestration on edge devices. They also support local-first LLM systems that handle sensor inputs, trigger real-time actions, and operate within autonomous runtime environments.

A smart home environment requires privacy-preserving orchestration. Imagine voice-controlled devices that adjust lighting, manage heating, and monitor security feeds. Homeowners may not want this sensitive data sent to the cloud. MCP enables local-first LLMs to access on-device tools and trigger safe actions directly, ensuring commands are executed quickly and privately.

Network collaboration

AI communication protocols enable peer-to-peer (P2P) agent networks, distributed decision-making, and resilient collaboration across blockchain and trustless environments. By standardizing interactions, these protocols ensure agents can exchange information, validate actions, and perform autonomous operations without a central authority.

A group of universities created an open research network where AI agents exchange models and datasets P2P. Instead of relying on a central server, they use ANP, an agentic communication protocol that leverages DIDs and encryption to enable trusted, secure collaboration across institutions.

Similarly, in decentralized finance (DeFi), agents representing banks, auditors, and exchanges must validate transactions and maintain compliance. ANP allows these agents to identify each other’s actions transparently, maintaining both resilience and security in a trustless ecosystem.

Challenges of AI communication protocols and how to handle them

When using AI communication protocols, it’s important to consider their inherent limitations. Recognizing these gaps enables teams to design more resilient and robust systems.

Identity & trust across organizations

A2A and ACP rely on existing enterprise authentication, which can fail in multi-organization environments, potentially disrupting agent collaboration and workflow continuity.

To address this, teams should standardize on OAuth/OIDC with authenticated extended Agent Cards, OAuth2/Bearer, mTLS, and JWT/JWKS where applicable. For zero-trust or decentralized deployments, experimenting with ANP’s DID-based authentication offers a more resilient, vendor-neutral alternative.

Long-running tasks & reliability

Managing multi-hour tasks and retries across agents can be challenging, risking incomplete workflows or data loss. Using A2A’s task life cycle alongside SSE streaming enables more robust orchestration. MCP users should follow its resumability guidance for Streamable HTTP to ensure reliable task execution, with redelivery via Last-Event-ID.

Observability & auditing

Complex multi-agent chains are difficult to trace, complicating debugging and compliance reporting. Standardizing metadata—such as ACP citations, trajectory tracking, correlating task IDs in A2A, and centralizing provenance logs—can maintain transparency and make multi-agent operations auditable.

Governance churn & vendor risk

With ACP merged into A2A, its road map and governance have shifted, creating uncertainty for future integrations. To mitigate risk, adopt A2A for all new cross-vendor deployments while retaining ACP compatibility layers for existing REST-based infrastructure. MCP remains stable for direct app-to-tool access.

Think of challenges as design constraints rather than obstacles. With the right mitigation strategies, each protocol can still be deployed effectively, depending on your context.