please create a rundown of this paper for me : ## A Survey of Agent Interoperability Protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)

This survey examines four emerging agent communication protocols, each targeting a different interoperability tier:

-  Model Context Protocol (MCP): a JSON-RPC client–server interface for secure context ingestion and structured tool invocation
  
- Agent-to-Agent Protocol (A2A): a peer-to-peer framework using capability-based Agent Cards over HTTP and Server-Sent Events for enterprise-scale task orchestration
  
-  Agent Communication Protocol (ACP): a REST-native performative messaging layer with multi-part messages, asynchronous streaming, and observability features for local multi-agent systems
  
- Agent Network Protocol (ANP): a decentralized discovery and collaboration protocol built on decentralized identifiers (DIDs) and JSON-LD graphs for open-internet agent marketplaces

Architectural details, integration approaches, communication patterns, and security considerations are reviewed for each protocol. A comparison highlights trade-offs in interaction modes, discovery mechanisms, communication models, and security frameworks. A phased adoption roadmap sequences MCP, A2A, ACP, and ANP to guide progressive deployment in real-world agent ecosystems.

==Lack of Context Standardization for LLMs:== Large Language Models (LLMs) require contextual grounding to produce accurate outputs. ==However, existing application architectures provide no unified mechanism to deliver structured context to LLMs, leading to ad hoc tool integrations and unreliable behavior.== Solution: The Model Context Protocol (MCP) addresses this by standardizing how applications deliver tools, datasets, and sampling instructions to LLMs, akin to a USB-C for AI. ==It supports flexible plug-and-play tools, safe infrastructure integration, and compatibility across LLM vendors.==

==Communication Barriers Between Heterogeneous Agents:== ==Enterprise systems often consist of agents built using different stacks and frameworks, resulting in isolated behavior and poor collaboration.== Solution: The Agent Communication Protocol (ACP) ==offers a RESTful, SDK-optional interface with open governance under the Linux Foundation. It enables asynchronous-first interactions==, offline discovery, and vendor-neutral execution, bridging interoperability gaps at scale.

==Absence of Unified Agent Collaboration Standards:== ==Even when agents communicate, there’s no shared framework for dynamic negotiation, capability sharing, and coordination.== Solution: The Agent2Agent ==(A2A) protocol introduces a multimodal communication standard to unlock dynamic interaction between opaque, autonomous agents—regardless of framework.== It simplifies enterprise integration and supports shared task management and user experience negotiation.

==Internet-Agnostic Agent Communication:== ==The modern internet is optimized for human interaction but suboptimal for autonomous agents,== which require low-latency, API-native communication and decentralized identity validation. ==Solution: The Agent Network Protocol (ANP) provides a layered protocol architecture incorporating decentralized identity (W3C DID), semantic web principles, and encrypted communication to facilitate cross-platform agent collaboration over the open internet.==

Despite these advances, ==current function-calling ecosystems suffer from several limitations==. Tool definitions are typically static: ==agents must be re-initialized whenever new APIs are added== or schemas change, preventing truly dynamic discovery. S==ecurity boundaries—such as authentication tokens, rate limits, and access control—are ad-hoc and framework-specific, increasing the risk of unauthorized calls.== ==Moreover, each framework employs its own metadata conventions==, hindering cross-framework reuse of tools and requiring bespoke adapters for interoperability. Addressing these challenges requires protocol-level standards that prescribe a common schema for function metadata, dynamic capability negotiation, and end-to-end security guarantees across heterogeneous LLM agent platforms.

Recent advances have extended the capabilities of LLMs beyond reasoning to include orchestration of external tool invocation. ==Toolformer employs a self-supervised masking strategy that exposes potential API calls during pretraining, enabling the model to learn when and how to invoke functions as part of its text generation==. 
==ReAct interleaves chain-of-thought reasoning with explicit action calls, allowing models to alternate between “thinking” steps and tool invocations based on intermediate observations.== These approaches unify reasoning and action at the single-agent level but do not address peer discovery or multi-agent coordination.


### MCP 

==The Client Application (Host) serves as the initiator of interactions in the MCP ecosystem.== It is responsible for managing connections to one or more MCP Servers and orchestrating communication workflows in accordance with protocol specifications. ==In practice, the client initializes sessions, requests and processes the four core primitives Resources, Tools, Prompts, and Sampling, and handles asynchronous notifications related to server-side events.== The client must also implement robust error-handling routines to gracefully manage communication failures or timeout conditions, ensuring reliable coordination with remote MCP Servers.
![[Pasted image 20250701130158.png]]
==The MCP Server functions as the provider of data, services, and interaction templates that the client can utilize to enrich LLM-based workflows.== It exposes and manages contextual Resources, executes external operations via Tools, defines reusable Prompts for consistent interaction patterns, and optionally delegates text-generation tasks through Sampling. Beyond serving requests, the server is responsible for enforcing access control policies, maintaining operational security, and emitting notifications that reflect changes in its available capabilities. This provider-side architecture complements the client’s orchestration logic by modularizing access to complex or dynamic resources.

==The Model Context Protocol is composed of several layered abstractions that govern the structure and semantics of communication.== ==At the foundation lies the Protocol Layer, which defines the semantics of message exchange using the JSON-RPC 2.0 specification.== It ensures that each request is linked to a corresponding response and that all interactions conform to predictable patterns. Above this, ==the Transport Layer handles the physical transmission of messages between the client and server==, supporting both local communication via Stdio and network-based channels such as HTTP with optional Server-Sent Events (SSE). ==At the highest abstraction, MCP organizes messages into four types: Requests, which are calls expecting replies; Results, which are successful responses to earlier requests; and Errors, which indicate failures or invalid invocations. A fourth type, Notifications, is used for asynchronous updates that do not require a client acknowledgment.==

==The MCP Server offers four core capabilities Tools, Resources, Prompts, and Sampling each mapped to a distinct control model== that governs the interaction between the client, the server, and the LLM.

**Tools** are model-controlled capabilities that ==allow the LLM to invoke external APIs or services==, often automatically and sometimes with user approval. This facilitates seamless integration with third-party systems and ==streamlines access to real-world data and operations.==

**Resources** are ==application-controlled elements==, such as structured documents or contextual datasets, ==that are selected and managed by the client application.== They provide the LLM with tailored, task-specific inputs and ==enable context-aware completions.==

**Prompts** are ==user-controlled templates defined by the server but selected by end-users== through the client interface. These reusable prompts promote consistency, reduce redundancy, and support repeatable interaction patterns.

**Sampling** i==s server-controlled and allows the MCP Server to delegate the task of generating LLM completions to the client==. This supports sophisticated agentic workflows and enables fine-grained oversight over the model’s generative process, including the ability to adjust temperature, length, and other sampling parameters dynamically.

==The Model Context Protocol (MCP) defines a three-phase lifecycle for client–server interactions, designed to ensure robust session management, secure capability negotiation, and clean termination.== These phases ==Initialization, Operation, and Shutdown== correspond to the temporal sequence of communication between the Client Application and MCP Server.

Initialization begins by establishing protocol compatibility and exchanging supported capabilities. During version negotiation, the client and server agree on the highest mutually supported protocol version. This is followed by a capability exchange, in which both sides advertise optional features—such as sampling, prompts, tools, and logging—that can be used during the session. The phase concludes with a notifications/initialized message sent by the client after receiving the server’s initialize response, signaling readiness to proceed to operational communication.

==Operation represents the core active phase, during which the client and server exchange JSON-RPC method calls and notifications in accordance with the negotiated capabilities.== Both parties are expected to adhere strictly to the features agreed upon during initialization, ensuring compatibility and predictability. Each task invocation may include a configurable timeout, and if a response is not received within that window, the client may issue a cancellation notification to prevent resource exhaustion or stale execution threads.

Shutdown ensures a clean and predictable end to the session. Either party may initiate termination by closing the transport layer typically HTTP or stdio which signals the end of communication. Upon shutdown, both client and server are responsible for resource cleanup, including the removal of active timeouts, cancellation of subscriptions, and deallocation of any spawned child processes. After this point, no new protocol messages should be sent, with the exception of essential diagnostics like ping or log flush events.

### A2A Architecture

==The Agent-to-Agent (A2A) architecture facilitates communication and collaboration between distinct agentic systems to accomplish tasks.== It comprises three primary actors—User, Client Agent, and Remote Agent (Server), that interact via a well-defined protocol, enabling secure and interoperable execution.
![[Pasted image 20250701132613.png]]
==The User initiates a task or request, typically without needing to understand or directly interact with the underlying agentic systems.== The Client Agent receives this request, analyzes its intent, and identifies ==a suitable Remote Agent (Server) by inspecting the advertised capabilities through its Agent Card. Once selected, the Client Agent engages the Remote Agent to execute the task, coordinating message exchanges and retrieving results—termed Artifacts—which are then delivered back to the User.==

==The User acts as the originator of any A2A interaction, embodying the intent or need that sets the agentic process in motion.== While frequently a human end-user, the User can also be a system, service, or another agent in hierarchical workflows. ==Regardless of its form, the User does not directly interact with Remote Agents;== ==instead, it relies on the Client Agent to translate its requests into actionable tasks== and to mediate all responses.

==A2A supports diverse User interaction models. A Direct End-User may engage with the Client Agent through interfaces such as chatbots or voice assistants, providing task input and receiving results in real time.== ==An Indirect End-User interacts with higher-level systems that transparently utilize A2A agents behind the scenes,== such as enterprise dashboards or orchestration tools. Systems or Services may also act as Users, invoking A2A agents autonomously for workflows like data transformation or monitoring. ==In multi-agent hierarchies, an Agent as User scenario occurs when one agent triggers downstream actions by another agent== to fulfill complex tasks. ==These various User paradigms underscore the protocol’s agnosticism toward User identity and emphasize its focus on standardizing communication between Client and Remote Agents.==

==The Client Agent serves as an intermediary that represents the User’s intent and coordinates with Remote Agents to fulfill it.== Its responsibilities span multiple stages of the task lifecycle. ==It begins by performing Agent Discovery, retrieving and evaluating Agent Cards that describe each Remote Agent’s skills==, capabilities, input/output specifications, and authentication requirements. Based on this discovery, ==the Client selects a Remote Agent aligned with the User’s task==. Next, the Client Agent is responsible for Task Initiation. It constructs a structured Task object, encapsulating the User’s intent, relevant metadata, and formatted inputs. It then sends this task to the selected Remote Agent using a well-formed Message. ==During execution, the Client Agent manages the Message and Artifact Exchange.== It communicates bi-directionally with the Remote Agent, sending new instructions or follow-ups, and receiving outputs—termed Artifacts—along with any intermediate updates. For long-running or stateful interactions, it maintains Session Context, using identifiers to group related exchanges under a unified workflow.

The Client Agent also oversees Error Handling, parsing any failure responses returned by the Remote Agent, and executing appropriate recovery strategies such as retries, fallback agent selection, or User notifications. After execution, the Result Presentation step involves transforming Artifacts into a user-consumable format and integrating them into the surrounding application or user interface.

Where supported, the Client Agent may handle Asynchronous Communication through mechanisms such as Server-Sent Events (SSE) or push notifications. For SSE, it establishes a persistent connection and streams updates in real time. If push notification support is available, the Client Agent registers with a notification service to receive task updates delivered out-of-band. Altogether, the Client Agent acts as the execution orchestrator, data translator, and communication bridge within the A2A protocol, enabling intelligent, context-aware interactions on behalf of the User.

The Remote Agent (Server) is the service endpoint that executes tasks delegated by the Client Agent. It provides one or more Skills, which represent discrete operations it can perform ranging from simple data retrieval to complex computations or orchestrations involving external APIs or databases. Each skill is formally defined by its input and output schema, enabling consistent invocation across clients.

To make these capabilities discoverable, the Remote Agent publishes an Agent Card—a structured metadata document that includes a list of available skills, usage instructions, input/output formats, supported protocols, and authentication requirements. This Agent Card acts as both an advertisement and an interface contract for interacting agents.

The Remote Agent must also manage its internal Resource Usage, ensuring fair allocation of compute, memory, network, and storage resources during task execution. Alongside execution, it is responsible for enforcing Security and Access Control mechanisms. This includes authenticating Clients, verifying message integrity, and authorizing access to specific skills based on access policies or token scopes. By abstracting service capabilities into modular, independently managed components, the Remote Agent supports composability, reliability, and interoperability within agentic ecosystems.

An A2A agent is structured around several core components that define its behavior, capabilities, and interactions. These components serve as the operational and semantic building blocks for any agent to function within an agent-to-agent ecosystem.

Agent Card acts as a self-description and discovery mechanism. It is a JSON-formatted document that publicly declares the agent’s metadata, including its name, version, description, supported skills, and authentication requirements. Client Agents rely on Agent Cards to discover and evaluate Remote Agents that can fulfill specific task criteria. As the primary entry point for coordination, an agent without an Agent Card is effectively invisible within the A2A system.

Skills represent the actionable capabilities offered by an agent. Each skill is described by a name, purpose, expected input parameters, and output format. Skills are invoked via Tasks and encapsulate the core utility the agent provides. An agent’s relevance and specialization are directly tied to the breadth and precision of its published skills.

Task is the atomic unit of work delegation. It specifies the skill to be executed, along with input parameters and contextual metadata. Tasks are issued by Client Agents and processed by Remote Agents, enabling asynchronous or synchronous collaboration. By structuring intention and invocation in a standardized format, Tasks allow A2A agents to operate interoperably across diverse systems.

Messages serve as the primary communication channel between agents. These encapsulate data exchange and coordination activities such as task submission, intermediate status updates, or artifact delivery—and can be composed of multiple typed parts including plain text, structured data, or file references. Without Messages, inter-agent interaction would not be possible, making them foundational to the A2A protocol. Artifacts are the tangible outputs of skill execution. Once a Remote Agent completes a task, it generates Artifacts that may contain structured responses, computed results, documents, or linked data. These outputs are transmitted back to the Client Agent, which may render them to the User or incorporate them into downstream processes. Artifacts represent the materialized knowledge or value created through agentic collaboration.

The A2A protocol supports multiple transport mechanisms to enable communication between Client and Remote Agents, tailored to support both synchronous and asynchronous workflows. When real-time streaming is required and supported by both parties, Server-Sent Events (SSE) can be employed. SSE establishes a persistent HTTP connection over which the Remote Agent can send live status updates or partial Artifacts to the Client Agent, facilitating continuous feedback during long-running tasks.

In scenarios where persistent connections are impractical such as mobile or distributed deployments—the protocol accommodates Push Notifications. These are implemented through a PushNotificationService interface that allows the Remote Agent to notify the Client about task progress or completion via out-of-band channels. This model is particularly suited for latency-tolerant workflows and background task orchestration.

All core task communications in A2A adhere to the JSON-RPC 2.0 specification. This ensures a standardized format for method invocation, parameter passing, and result encapsulation. Additionally, Remote Agent discovery is bootstrapped via HTTP GET requests directed at the Agent’s endpoint, specifically retrieving its Agent Card as a structured representation of supported capabilities. Together, these mechanisms enable flexible, interoperable, and extensible communication across diverse runtime environments.

The lifecycle of a Remote Agent in the A2A protocol follows a structured progression through four key phases: Creation, Operation, Update, and Termination. Each phase reflects a distinct set of responsibilities critical to ensuring secure, discoverable, and reliable agent behavior.

Creation begins with the publication of the Agent Card, a JSON-formatted document served at /.well-known/agent.json, which declares metadata such as the agent’s name, version, supported skills, and authentication schemes. Once the Agent Card is made available, the agent service is deployed at a designated endpoint and configured to handle JSON-RPC 2.0 requests over HTTP. To complete the creation phase, the Remote Agent must implement the declared authentication mechanisms, enabling secure client verification and access control.

During Operation, the Remote Agent processes Tasks submitted by Client Agents in accordance with its advertised skills. This includes receiving structured Task payloads, executing the associated skill logic, and managing ongoing communication through JSON-RPC status messages and Artifact delivery. If supported, the agent may also stream asynchronous updates using Server-Sent Events (SSE) or deliver them through a registered PushNotificationService. The agent is responsible for maintaining internal task state throughout execution to ensure consistency and traceability across interactions.

In the Update phase, the Remote Agent refreshes its capabilities or configurations. This includes incrementing the version field in the Agent Card, adding new skills or authentication modes, and applying security patches to maintain compliance. The agent may also deprecate outdated features or legacy interfaces, ideally signaling these changes to clients through updated documentation or explicit lifecycle status fields.

Finally, during Termination, the Remote Agent gracefully winds down its operations. In-flight Tasks are driven to completion or transitioned to a terminal state, and any open SSE streams are closed. The service then deregisters its Agent Card—by removing or archiving the published metadata and releases allocated system resources, ensuring a clean shutdown with no residual exposure or stale endpoints. A well-defined Remote Agent lifecycle enhances discoverability, promotes interoperability, and ensures that agent-based collaboration remains secure, consistent, and predictable within the broader A2A ecosystem.

### ACP Core Architecture

The Agent Communication Protocol (ACP) defines a layered, REST-native framework for interoperable AI agents. Its static architecture comprises distinct actors and protocol layers, each responsible for a well-defined set of functions.
![[Pasted image 20250701134014.png]]
The Agent Communication Protocol (ACP) defines a streamlined three-role architecture designed to standardize discovery, invocation, and interaction among AI agents. This architecture ensures that clients can seamlessly locate agents, initiate structured task requests, and receive multimodal responses without requiring bespoke integrations.

At the entry point of this architecture is the Agent Client, which initiates communication by discovering agents through a published registry and composing structured requests in ACP compliant format. It encapsulates user intent into multi-part messages, manages session-level context if needed, and processes ordered response parts ranging from plain text to rich artifacts and binary data returned by agents.

At the center of the ACP system is the ACP Server, which acts as a protocol broker. It maintains the Agent Registry, a metadata catalog defined by the Agent Detail Schema, and enforces system-wide policies including authentication, authorization, and rate limiting. Upon receiving a client request, the ACP Server performs agent lookup and routing, ensures compliance with registered capabilities, and facilitates secure transport of response parts back to the client in the prescribed order.

The ACP Agent represents the execution endpoint, where domain-specific logic resides. It may operate as a stateless microservice or maintain session context to support multi-turn interactions. The agent ingests structured requests composed of ordered message parts each tagged with semantic metadata and processes them in accordance with the registered skill definitions. Upon task completion, the agent emits response parts that conform to ACP’s Message Structure specification, enabling a uniform and interpretable response pipeline.

Together, these three components establish a modular and interoperable framework for agent communication, promoting scalable deployment, loose coupling, and clear separation of discovery, orchestration, and execution logic.

ACP interactions are governed by a set of core components that define agent behavior, enable runtime interoperability, and standardize task communication. Central to this architecture is the Agent Detail, a self-descriptive JSON or YAML document that serves as the agent’s public identity and capability profile. It provides essential metadata, including the agent’s name, available operations, supported content types, authentication schemes, and runtime diagnostics. Clients rely on Agent Detail as a precondition for invocation, enabling trust and selection without bespoke integration.

Complementing this, Discovery Mechanisms allow clients to locate agents dynamically at runtime. These mechanisms may be centralized—such as registry APIs—or decentralized, including manifest files hosted under well-known URLs (e.g., /.well-known/agent.yml) or embedded within deployment metadata like container labels. This discoverability layer decouples client logic from fixed configurations and supports scalable agent networks.

Once an agent is located, clients issue a Task Request, a structured unit of delegated work. Task Requests are composed of ordered message parts that specify the target operation and include textual inputs, binary payloads, or references to externally hosted data. This design accommodates both synchronous calls and long-running, asynchronous tasks.

All requests and responses conform to ACP’s Message Structure, which standardizes the communication envelope. Each message is an ordered list of parts, with explicit MIME content_type annotations and either embedded content or dereferenceable content_url values. Optional name attributes enable the use of semantically tagged Artifacts, facilitating downstream interpretation.

Finally, the result of agent execution is encapsulated in one or more Artifacts. These may consist of structured JSON outputs, plain text completions, binary files, or even nested message references. Artifacts are delivered as part of the Message Structure response and are subsequently rendered, stored, or chained into additional agent workflows, ensuring extensibility and composability across ACP-enabled systems.

The lifecycle of an ACP agent closely mirrors the A2A framework’s four canonical phases: Creation, Operation, Update, and Termination. Each stage ensures that agent behavior remains discoverable, interoperable, and secure throughout its active deployment.

Creation begins with the configuration and deployment of the agent. This involves declaring the agent’s capabilities and metadata through an Agent Detail manifest, which is made accessible via an ACP-compliant server, such as an ASGI-based or built-in implementation. The agent is initialized with authentication mechanisms and routing logic that collectively secure both service discovery and downstream task execution.

During Operation, agents process structured sendTask requests submitted by clients. These requests contain encoded parameters required for task execution. The ACP runtime supports synchronous execution as well as incremental streaming of intermediate results. Each task progresses through well-defined states—such as created, in_progress, or awaiting that are managed by the ACP execution engine. For multi-turn workflows, session-level persistence ensures continuity of context across multiple interactions.

In the Update phase, the Agent Detail manifest is refreshed to reflect changes in the agent’s behavior or capabilities. These updates may include new operations, supported MIME types, or version increments. Importantly, the discovery process is resilient to such changes: clients querying the agent registry retrieve the latest manifest without requiring direct API modification, thereby preserving backward compatibility.

Termination involves the graceful decommissioning of the agent. All active tasks are driven to completion, ongoing streams are closed, and the agent’s manifest is deregistered or marked as inactive to prevent future discovery. Any allocated resources are released, and session data is finalized to ensure a clean and auditable shutdown.

### ANP Architecture

Agent Network Protocol (ANP) is a decentralized, peer-to-peer communication standard designed for cross-platform agent interoperability on the open internet. ANP enables agents to autonomously discover, authenticate, and interact using structured metadata and AI-native data exchange. The following sections align ANP’s architecture with a standardized lifecycle and modular framework, modeled consistently with MCP, A2A, and ACP.
![[Pasted image 20250701134205.png]]

The Agent Network Protocol (ANP) is underpinned by a set of foundational components that collectively support decentralized identity, semantic self-description, discovery, and adaptive interaction. At the core is the Agent Identity, which employs Decentralized Identifiers (DIDs) to uniquely identify agents across platforms. Specifically, ANP adopts the did:wba method, where each identifier corresponds to an HTTPS-hosted DID document, thereby leveraging existing Web infrastructure for decentralized identity resolution.

Building upon this identity layer is the Agent Description, implemented through the Agent Description Protocol (ADP). These JSON-LD formatted documents contain structured metadata about the agent, including its name, capabilities, supported protocols, authentication schemes, and service endpoints. They serve as the agent’s publicly accessible profile, facilitating interoperability and semantic understanding.

Agents expose their presence and capabilities through a Discovery Directory, typically located at the standardized .well-known/agent-descriptions endpoint. This directory enables both human users and automated systems to retrieve a list of available agents under a given domain, forming the basis for scalable agent indexing and search.

To support interaction, ANP accommodates two categories of communication interfaces: Structured Interfaces, such as JSON-RPC and OpenAPI, and Natural Language Interfaces, defined via YAML or equivalent schema files. Both interface types are declared within the agent’s description and enable flexible interaction patterns suited to varying complexity and use cases.

Finally, the Meta-Protocol Negotiator facilitates dynamic protocol alignment between agents. This mechanism allows agents to exchange natural language descriptions of their communication requirements and capabilities, from which compatible interaction protocols can be negotiated and instantiated. By supporting runtime adaptability and negotiation, this layer ensures seamless interoperability even among heterogeneous agent ecosystems.

The ANP agent lifecycle adheres to the canonical phases of Creation, Operation, Update, and Termination, reflecting the decentralized design principles of the Agent Network Protocol. Each phase ensures that agents remain discoverable, verifiable, and interoperable within a globally distributed agent ecosystem.

Creation initiates with the generation of a decentralized identifier (DID) using the did:wba method. This identifier is associated with a publicly resolvable HTTPS endpoint hosting the agent’s DID document. In parallel, the agent prepares a self-descriptive Agent Description (ADP) document in JSON-LD format, detailing its services, supported protocols, and authentication mechanisms. The ADP is then published under a standardized path such as /.well-known/agent-descriptions, enabling web-based discovery or optional registration with search agents.

During the Operation phase, agents authenticate and interact via cryptographic credentials defined in their DID documents. All communications follow structured interaction models declared in the ADP—such as JSON-RPC for precise invocation or YAML-based interfaces for natural language negotiation. Secure transport is established using HTTPS and, where applicable, real-time communication is supported through mechanisms such as Server-Sent Events (SSE) or long polling. Agents act autonomously or cooperatively by invoking external services, interpreting requests, and returning results in a standardized format.

The Update phase allows agents to revise their ADP documents and associated DID metadata to reflect evolving capabilities or interface changes. These updates are automatically propagated through recurring crawls by indexing services or explicitly refreshed via active discovery endpoints. Because agent identity and service descriptions are independently versioned and published, clients can dynamically adapt to updates without breaking existing integrations.

Termination involves the intentional deactivation of an agent. This includes the removal or archival of its DID document and the depublication of its ADP endpoint from discovery directories. Any issued authentication tokens, access credentials, or associated metadata must be revoked to ensure security. A clean shutdown preserves the integrity of the agent ecosystem by preventing stale or orphaned entries from persisting in discovery indexes or trusted registries

### comparision 
|Aspect|MCP (Model Context Protocol)|ACP (Agent Communication Protocol)|A2A (Agent-to-Agent Protocol)|ANP (Agent Network Protocol)|
|---|---|---|---|---|
|Architecture Model|Client–Server with JSON-RPC primitives|Brokered Client–Server (Registry + Task Routing)|Peer-like Client ↔ Remote Agent|Decentralized Peer-to-Peer|
|Agent Discovery|Manual registration or static URL lookup|Registry-based|Agent Card retrieval via HTTP|Search Engine Discovery|
|Identity & Auth|Token-based auth; supports DIDs optionally|Bearer tokens, mutual TLS, JWS|DID-based handshake or out-of-band headers|Decentralized Identifiers (DID), especially did:wba|
|Message Format|JSON-RPC 2.0 with Prompts, Tools, Resources|Structured multipart messages with MIME-typed parts|Task + Artifact messaging over JSON|JSON-LD with Schema.org and ADP/Meta-Protocol negotiation|
|Core Components|Tools, Prompts, Resources, Sampling|Agent Detail, Message, Task Request, Artifact|Agent Card, Task, Message, Artifact|DID Document, Agent Description, Meta-Protocol, Structured Interface|
|Transport Layer|HTTP, Stdio, Server-Sent Events (SSE)|HTTP with incremental streams|HTTP with optional SSE + Push Notifications|HTTP with JSON-LD over TLS|
|Session Support|Stateless + optional persistent tool context|Session-aware with run state tracking|Session-aware or stateless; client-managed IDs|Stateless; DID-authenticated tokens used across connections|
|Target Scope|LLM ↔ External Tool/Service integration|Model-Agnostic, Infrastructure-level agents|Trusted enterprise task delegation|Open Internet agent interconnectivity|
|Primary Use Case|Augment LLMs with external capabilities (e.g., code, search)|Secure, typed message exchange for diverse agents|Multi-agent workflows within organizational trust boundaries|Cross-platform agent discovery, secure P2P execution|
|Strengths|Tight LLM integration; resource injection|Multimodal messaging; brokered registry; tool modularity|Inter-agent negotiation; artifact-driven delegation|DID-based trustless identity; AI-native protocol negotiation|
|Limitations|Centralized server assumption; prompt injection risks|Registry required; strong assumptions on server control|Enterprise-centric; assumes agent catalog|High negotiation overhead; evolving adoption ecosystem|

### Phased Adoption Roadmap for Agent Interoperability

This section presents a practical, multi-stage deployment strategy for agent interoperability based on protocol maturity, integration complexity, and domain-specific use cases. The roadmap helps organizations gradually adopt the most suitable agent communication standards while ensuring scalability, composability, and security.

The initial phase involves adopting the Model Context Protocol (MCP) to enable secure and structured interaction between large language models (LLMs) and external tools or resources. MCP leverages a JSON-RPC-based client-server model, making it ideal for early agent integration where tool invocation, deterministic execution, and typed inputs/outputs are critical.

Once foundational tool capabilities are in place, the Agent Communication Protocol (ACP) can be layered to support asynchronous, multimodal, and REST-native messaging. ACP introduces ordered message parts, flexible task schemas, and streaming support—features that enable richer agent conversations and integration with broader RESTful ecosystems.

In more complex enterprise environments, the Agent-to-Agent (A2A) protocol enables multi-agent workflows and task orchestration through structured capability cards and artifact exchanges. A2A supports dynamic discovery via Agent Cards and secure intra-organizational collaboration in trusted contexts.

The final phase involves extending interoperability to the open internet using the Agent Network Protocol (ANP). ANP facilitates decentralized agent discovery, DID-based identity verification, and peer-to-peer communication using JSON-LD graphs. It provides the foundation for scalable, cross-platform agent marketplaces and AI-native web interaction. This phased approach enables organizations to adopt agent communication protocols progressively, maximizing interoperability while minimizing integration complexity at each stage.

### Conclusion 

As autonomous agents powered by large language models proliferate across domains, the demand for secure, modular, and interoperable communication grows increasingly urgent. This survey presented a structured analysis of four emerging protocols—MCP, ACP, A2A, and ANP—that each address distinct layers of agent interoperability. By unifying tool invocation, multimodal messaging, task coordination, and decentralized discovery, these protocols collectively form the foundation for scalable multi-agent systems. The comparative evaluation demonstrates that no single protocol suffices across all contexts; instead, a phased, complementary adoption strategy—beginning with MCP and progressing through ACP and A2A to ANP—offers a practical pathway for deploying agent ecosystems. Future research should explore protocol interoperability bridges, trust frameworks for agent collaboration, and standardized evaluation benchmarks to accelerate adoption and ensure resilience in real-world deployments. These foundational efforts will be critical for advancing the next generation of intelligent, networked agents.