AI Agent Protocols: MCP vs A2A vs ANP vs ACP

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MCP vs A2A vs ANP vs ACP: Choosing the Right AI Agent Protocol

By Dr. Hernani Costa — Jul 6, 2025

Compare MCP, A2A, ANP, and ACP—today's leading AI agent protocols. Learn strengths, limitations, and use cases to choose the best fit for your agent stack.

The battle for AI agent interoperability is heating up. Four major protocols are vying to become the universal standard for how AI agents communicate, collaborate, and access tools. Just as the early internet needed HTTP to connect disparate systems, today's emerging "agent internet" needs its own communication layer to avoid a tangle of custom integrations. In this analysis, I compare the four leading AI agent communication protocols — Model Context Protocol (MCP), Agent-to-Agent Protocol (A2A), Agent Network Protocol (ANP), and Agent Communication Protocol (ACP) — by examining their developers, architectures, discovery mechanisms, session handling, transport layers, strengths, limitations, and use cases.

Notably, Anthropic's MCP focuses on linking a single AI agent to external tools (providing context to an LLM), whereas Google's A2A and Cisco's ANP enable direct agent-to-agent collaboration (albeit one via a centralized directory, the other fully decentralized). IBM's ACP takes an enterprise-friendly middle ground, using a brokered model for multi-agent orchestration. Each comes with distinct advantages and trade-offs for businesses.

Key attributes of four leading AI agent communication protocols (MCP, A2A, ANP, ACP) are compared side by side. Each protocol offers a distinct approach across these dimensions, from system architecture to discovery and session management. For example, Anthropic's MCP employs a simple client-server model, best suited for tool integration, whereas Cisco's ANP adopts a decentralized "network of agents" vision with greater complexity. Such differences carry significant strategic implications, which I explore below for each protocol.

Model Context Protocol (MCP)

Developer: Anthropic (open-source initiative, introduced late 2024)
Architecture: Client–Server (LLM client connects to external tool servers via a standardized interface)
Agent Discovery: Manual registration of tools/agents (no auto-discovery; tools must be predefined to the LLM)
Session Support: Stateless — Each request is independent by default, with no persistent session or shared memory between calls. (An optional persistent context can be maintained per tool, but there's no built-in multi-turn session state tracking.)
Transport Layer: HTTP and JSON-RPC for request/response; also supports StdIO (for local tools) and SSE (Server-Sent Events) for streaming outputs. (JSON-RPC provides structured remote procedure calls in JSON format, while SSE allows real-time streaming of responses.)
Strengths: Tool-calling mastery. MCP excels at reliably linking a language model to external tools, databases, and APIs. Think of MCP as a "USB-C port" for AI applications — a universal connection to provide the model with external data and actions. It features a simple design and a rich ecosystem (Anthropic and the community have built many MCP-compatible tool servers for services like Google Drive, Slack, GitHub, and databases). Organizations can integrate AI with existing systems quickly and with minimal custom code. The protocol's simplicity also makes it easier to adopt; it's currently the most widely used agent protocol in real-world applications.
Limitations: Narrow scope & statelessness. MCP's focus is both a feature and a constraint: it does not support agent-to-agent communication or coordination between multiple AI agents. In enterprise terms, it's not meant to let two AIs communicate with each other — only to allow one AI to interact with tools. This singular focus means complex workflows spanning multiple agents or long-running interactive sessions are outside MCP's purview. Additionally, by design, it has no built-in memory or session persistence between calls, which can complicate stateful interactions (developers must handle any context persistence on their own). Security and identity management are rudimentary — it assumes a trusted catalog of tools. While MCP does optionally support Decentralized Identifiers (DID) for authenticating tool access, it lacks a robust authorization or reputation system out of the box. Enterprises must therefore enforce access controls at the API level of the tool.
Use Cases: Secure tool integration for single-agent assistants. MCP is ideal for organizations seeking to integrate an AI assistant with multiple internal tools and data sources without having to rebuild their existing IT stack. For example, a customer service chatbot can utilize MCP to query a CRM database, retrieve knowledge base articles, or create support tickets in another system — all through standardized MCP calls to those tools. This yields quick wins: the AI agent gains actionable connectivity to business data, boosting its usefulness, while the straightforward MCP interface ensures maintainability. In essence, MCP excels in scenarios where a single primary AI agent must integrate with multiple enterprise systems to access information or take action. It delivers immediate ROI by letting AI access corporate tools safely and in a controlled manner, albeit without facilitating any "AI-to-AI" teamwork beyond that.

Agent-to-Agent Protocol (A2A)

Developer: Google (developed in collaboration with an industry consortium of 50+ companies).
Architecture: "Centralized Peer-to-Peer" — agents communicate directly (peer-to-peer message exchange), but centralized coordination is used for discovery and task orchestration. In practice, this means that each agent runs independently, yet a common Agent Directory (and standardized schemas) helps them find and understand one another.
Agent Discovery: Agent Cards — a discovering agent retrieves a target agent's "business card" (a JSON file, usually located at a well-known URL like /.well-known/agent.json) that details the agent's capabilities, API endpoints, supported formats, and authentication requirements. These Agent Cards enable capability discovery: an agent can determine which other agent is most suitable for a specific task by reading standardized metadata. (Essentially, the Agent Card functions as a profile that promotes what an agent can do, similar to a service catalog entry.)
Session Support: Mixed — A2A supports both stateless interactions and session-based tasks. It features a structured Task management lifecycle (with states like "Working", "Completed", "Failed", etc.) to monitor multi-step workflows across agents. Agents can retain context within a task if needed, enabling multi-turn exchanges when necessary. Additionally, A2A allows asynchronous operation: agents can pause a task to request input (e.g., user approval) and resume it later, thanks to its task lifecycle design.
Transport Layer: HTTP(S) with JSON and Server-Sent Events for streaming/push updates. A2A primarily uses standard web protocols — agents exchange JSON-formatted messages over HTTP or webhook callbacks, and can leverage SSE for real-time notifications or streaming results. This web-native approach (built on HTTP/JSON) eases integration with existing web services and firewalls.
Strengths: Dynamic agent collaboration. A2A is designed to enable structured, secure cooperation among AI agents from the ground up. It excels at capability discovery and negotiation: using Agent Cards and a universal "language" for agents, it allows one agent to automatically identify and invoke the appropriate specialized agent for a subtask. This makes A2A well-suited for orchestrating complex workflows where different AI agents assume distinct roles (e.g., a planner agent, a solver agent, a reporter agent). It also introduces robust task management — every inter-agent task has a clear lifecycle and ID, allowing progress to be tracked and audited. From a business perspective, A2A's design supports enterprise needs, as it includes security features such as authentication via DIDs and role-based access. Additionally, it's supported by a growing ecosystem of partners contributing to its specifications, demonstrating momentum and likely future stability. The protocol emphasizes asynchronous, long-running interactions, aligning with real-world business processes that can take minutes or hours to complete. In summary, A2A's strengths are in enabling multiple AI services to cooperate seamlessly on complex tasks, with enterprise-grade security and structure.
Limitations: Catalog dependence and emerging standard. As an early-stage protocol (mid-2025), A2A is still developing and not yet widely used in production. A practical constraint is that it relies on the existence of an agent registry or catalog: an organization must keep a list of available agents with their Agent Cards, or agents must already know where to find peers. This central catalog requirement can be a bottleneck, as it creates a single point of coordination and control. It works best in environments where participating agents are known, such as within a consortium or marketplace. Unlike a truly open web search, A2A won't automatically discover unknown agents unless they are registered somewhere accessible. Strategically, companies adopting A2A may need to invest in building an "agent ecosystem" upfront. Additionally, since Google and its partners support the protocol, there's some industry politics involved: the standard could change, and companies might be cautious about locking in or waiting for the standard to stabilize. In summary, while promising, A2A's full benefits depend on having a solid agent directory and on the protocol continuing to evolve. Side note: In June 2025, Google Cloud announced its A2A donation to the Linux Foundation, which could help ensure that this essential component remains vendor-agnostic and community-driven.
Use Cases: Multi-agent workflows and marketplaces. A2A is a strong choice when you need specialized AI agents to collaborate on a shared objective. For example, consider an automated hiring workflow: one agent parses job descriptions and shortlists candidates, another schedules interviews, and a third handles follow-ups — all of which coordinate via A2A messages. Google demonstrated exactly this scenario, with multiple specialized agents seamlessly handing off tasks in a recruiting pipeline. In an enterprise setting, A2A could connect departmental AI agents (e.g., a finance agent, an HR agent, a sales agent) to manage cross-functional processes without human micromanagement. It's also relevant for building agent marketplaces or ecosystems where third-party AI services can be dynamically plugged in. In such cases, A2A provides the common rules of engagement, allowing any compliant agent to join the workflow. Business strategists should view A2A as enabling a modular AI workforce, as it allows organizations to mix and match AI capabilities, albeit within a somewhat controlled network of known agents.

Agent Network Protocol (ANP)

Developer: Open-Source Community led by Cisco researchers. Initially proposed by Cisco, ANP is now an open project aiming to define the "HTTP of the agent internet era", with contributions from academia and industry.
Architecture: Decentralized Peer-to-Peer. ANP is built for a truly decentralized agent ecosystem — no central server or broker is required for agents to connect. Instead, every agent is a peer on a network, and trust is established through decentralized technologies (identity and encryption standards) rather than through a central authority. In practice, ANP defines multiple layers: an identity layer (based on W3C Decentralized Identifiers for agent IDs and keys), a negotiation layer (a meta-protocol for agents to agree on how to communicate), and application protocols for the content of messages. This layered design aims to mimic the openness of the internet, enabling any agent to find and communicate directly with any other agent.
Agent Discovery: Open discovery via web search (DID indexing). Rather than a fixed registry, ANP-enabled agents publish discoverable metadata (using JSON-LD and DID documents) that can be indexed by search engines or decentralized lookup services. For example, an agent might publish its DID and capabilities on a webpage or a blockchain; an interested party can find agents by searching the open web or via a decentralized directory that respects ANP standards. This approach is akin to how we discover websites — scalable and not controlled by any single entity. It allows an "Internet of AI Agents" where new agents can join simply by making themselves discoverable.
Session Support: Stateless with cryptographic trust. Interactions in ANP are generally stateless request-response exchanges (similar to how HTTP itself is stateless). However, because agents might require ongoing exchanges, ANP relies on protocol negotiation to manage multi-step interactions: two agents can negotiate a temporary session or agree to switch to a stateful, higher-level protocol if needed. Importantly, identity and trust are managed via DIDs and cryptographic keys, ensuring secure authentication and end-to-end encryption for each message. This means even without a central broker, agents can verify who they're talking to and establish secure sessions on the fly. Still, coordinating long, multi-turn dialogues is more complex under ANP, as there's no persistent session context unless explicitly managed by the agents.
Transport Layer: HTTP + JSON-LD (AI-native semantics). ANP generally uses HTTP for transport (making it easy to traverse networks), but the content is semantically rich JSON-LD (Linked Data) rather than plain JSON. JSON-LD enables the embedding of schema and context, which helps agents interpret messages without prior arrangement, a crucial feature for an open network. Additionally, ANP is extensible to other transports; its negotiation layer allows two agents to switch to a different transport (such as a direct socket or an alternative protocol) if they both support it. Overall, HTTP with JSON-LD is the default, giving a web-friendly, machine-interpretable communication medium.
Strengths: True decentralization & AI-native design. ANP is the "decentralized revolutionary" among these protocols. Its foremost strength is that it enables a completely open agent ecosystem — any AI agent, anywhere, can potentially interact with any other, without needing permission from or integration with a central platform. This opens the door to massive scalability (billions of agents) and avoids lock-in to a single vendor's framework. ANP is built on W3C standards for decentralized identity (DID), providing baked-in security and trust in a distributed environment. Another key strength is its AI-native protocol negotiation, where agents can utilize AI techniques to negotiate the communication protocols (e.g., agreeing on schemas or compressing messages), thereby making the network self-organizing and adaptable. In essence, ANP's design is unconstrained by human-oriented interfaces; it assumes machine-to-machine communication at scale and optimizes for that (no assumptions of GUIs or manual setup). For forward-looking enterprises, ANP offers strategic autonomy — the ability to participate in a wide, peer-to-peer AI network (think of it like joining an "AI internet" rather than a gated community). This could foster innovation through open collaboration and marketplace dynamics, much like the early web did for information exchange.
Limitations: Complexity and immaturity. The very features that make ANP powerful also introduce significant complexity. There is high negotiation overhead because agents may negotiate everything from encryption to message format on the fly, and interactions can involve multiple back-and-forth steps before any useful work is done. This overhead could impact performance and reliability, especially in early implementations. Moreover, managing identity via DIDs and ensuring interoperability without central governance is technically challenging (e.g., agents might interpret "capabilities" differently without a centralized schema authority). For now, ANP is an early-stage and largely experimental technology; few organizations have deployed it in production settings. Enterprises may be wary of the lack of proven tooling and the difficulty of troubleshooting a decentralized network. Another consideration is security and compliance — while decentralization can enhance security (by eliminating a single point of attack), it also means the absence of central control, which can conflict with corporate governance policies. Strategically, adopting ANP requires a long-term vision for an open agent ecosystem and a tolerance for the uncertainties associated with an evolving standard. It may not yield short-term ROI compared to more centralized solutions.
Use Cases: Decentralized agent ecosystems and marketplaces. ANP is best suited for scenarios where no single entity controls all agents, such as a decentralized marketplace of AI services or a cross-organizational network of agents. Consider a consortium of research institutions, each with its own AI agents (for climate data, simulation, and analysis) that need to collaborate without a central hub. ANP would allow them to discover each other and share data securely, forming an autonomous research network. Another example is an open marketplace for AI APIs: independent providers publish their agent services (with DIDs and descriptions), and consumers (agents or applications) find and invoke them via ANP search and negotiation. In such use cases, the value is in scale and openness, enabling innovation and partnerships across company boundaries. Enterprises exploring Web3-like decentralization in AI or aiming to avoid dependency on big tech standards might experiment with ANP. However, for most traditional businesses, ANP's benefits will be more long-term and strategic than immediate; it's a glimpse of a future "internet of agents" which is still on the horizon.

Agent Communication Protocol (ACP)

Developer: IBM (via the open-source BeeAI project under the Linux Foundation)
Architecture: Brokered Client–Server. ACP employs a hub-and-spoke model: a central agent broker (or registry) mediates communication between agents. Agents (clients) register with the broker and send messages to it; the broker routes messages to the intended recipient agent. This yields a controlled environment where discovery, authentication, and message routing are centrally managed and controlled. The philosophy is akin to a corporate email server or message bus for AI agents — standardized endpoints and a central directory ensure that every agent can reliably reach others via the broker.
Agent Discovery: Registry-based. An ACP registry holds records of available agents, their endpoints, and supported capabilities. When an agent needs a task done, it queries the registry to find an appropriate agent or directly addresses one by name if it is known. This centralized directory can be optimized for enterprise needs (ensuring only vetted, authorized agents are listed). ACP's BeeAI reference implementation even supports capability tokens — the registry can store tokens or permissions indicating what each agent is allowed to do, thereby enhancing security in discovery. In summary, discovery in ACP is centralized but efficient, much like a corporate intranet directory for services.
Session Support: Session-aware with state tracking. Unlike the stateless MCP, ACP is designed for long-running, multi-turn interactions. It supports maintaining conversation or task state across multiple messages. Agents can have ongoing "threads" or sessions for a task, with the broker able to track these sessions and route messages accordingly. This is crucial for complex workflows where context must persist (e.g., an agent handling a multi-step approval process). Additionally, ACP supports both synchronous and asynchronous communication modes — an agent can call another and wait for an immediate response, or initiate a task and continue when a response arrives later, with the broker facilitating callbacks and notifications. By tracking run state and sessions, ACP enables robust workflow orchestration, similar to business process management systems, but tailored for AI agents.
Transport Layer: HTTP with streaming and multi-part messages. ACP is built on RESTful HTTP APIs for simplicity — every agent exposes standardized HTTP endpoints (so any tool like cURL or Postman can interact with it easily). What sets ACP apart is its support for multipart MIME messages and streaming. Agents can exchange rich, structured messages consisting of multiple parts (for example, a message might include a JSON instruction part, plus a PDF document, plus an image, all as separate MIME parts in one HTTP transaction). This is analogous to how email can carry attachments of various types. It means ACP can handle multi-modal data natively (text, images, binaries in one message). It also supports incremental streaming of data when needed, allowing significant responses or real-time outputs to be sent chunk by chunk over HTTP. This transport approach is highly enterprise-friendly, leveraging ubiquitous web standards (HTTP, MIME) and integrating seamlessly into corporate IT infrastructure, while accommodating complex data exchange.
Strengths: Enterprise integration and modularity. ACP's design reflects IBM's pragmatic approach to enterprise. Key strengths include its RESTful simplicity — developers can use it without learning a new SDK or framework (any standard HTTP client works) — and its focus on modularity and interoperability. Because it utilizes open standards and is governed by the Linux Foundation, ACP is framework-agnostic, enabling seamless agent replacement and cross-platform integration. An organization can have agents built in different languages or from different vendors all communicate through ACP, which is crucial for avoiding vendor lock-in and technical silos. The central registry approach, while not as trendy as decentralization, is actually a strength in environments where governance, compliance, and reliability are top priorities. It allows consistent security enforcement (only registered agents can interact; their capabilities can be permissioned via tokens ) and easier monitoring. ACP's support for rich message content (file attachments, multimedia) and session management makes it ideal for complex enterprise workflows — for example, an AI agent in finance can send a spreadsheet or a signed document to an HR agent as part of an approval process, all within the same message exchange. Strategically, ACP gives large organizations a way to harness multi-agent systems without tearing up their existing IT rulebook: it works with established web tech and permits gradual adoption (one team can start using ACP for their agents and later integrate with others).
Limitations: Centralization and early development stage. The flip side of ACP's design is its dependence on a central broker or registry. This introduces a potential single point of failure and may raise scalability concerns if thousands of agents all rely on a single broker service. For companies, it means an additional piece of infrastructure to maintain (though likely one that IBM and the open-source community will provide in ready-to-deploy form). Also, while ACP avoids vendor lock-in at the protocol level, using it effectively might tie an organization to the IBM-led ecosystem (at least for support and tooling around BeeAI). Another consideration is that ACP is still in alpha/early development as of 2025. Standards are evolving, and best practices are still being discovered. Early adopters might encounter breaking changes or need to actively participate in the open-source project. Compared to MCP (which is simpler) or A2A (with broad backing), ACP's community is nascent. Businesses must weigh the benefits of their robust feature set against the risk of their youth. In terms of performance, the brokered model could introduce latency overhead compared to direct agent-to-agent calls, though in many enterprise use cases, this is an acceptable trade-off for better control.
Use Cases: Cross-departmental AI workflows with governance. ACP is a natural fit for internal enterprise agent networks — situations where a company wants various AI agents (from different teams or vendors) to work together on business processes in a controlled manner. For example, a financial services firm might use ACP to connect an investment research agent, a compliance agent, and a customer service agent, so they can jointly handle a client request. The ACP registry ensures that each agent only accesses data it's permitted to access (via capability tokens). In another scenario, a company might integrate an IBM-provided AI agent with its own custom AI services, and even third-party SaaS AI — ACP would allow all to communicate through a common, secure interface. Essentially, ACP shines in multi-agent orchestration inside an organization, where reliability, security, and auditability are paramount. It enables AI-driven automation across departments (e.g., an HR onboarding agent handing off to an IT setup agent and a finance payroll agent) with a clear record of all agent interactions through the central hub. For enterprises that require tool integration plus agent collaboration — for instance, an agent that not only uses tools (like MCP would) but also consults other agents — ACP provides a one-stop solution. It bridges the gap between tool-centric and agent-centric communication, albeit within the safe confines of an enterprise-controlled network.

My Take

Each of these protocols offers a unique value proposition, and the "best" choice really depends on the context. Here's my perspective on where they fit best:

For decentralized marketplaces and cross-organization networks, ANP is the visionary pick. If you're looking to build the "Internet of AI agents" — say a marketplace where anyone can publish an AI service and others can discover it — ANP's trustless, decentralized architecture is unmatched. Its use of open standards (DIDs, JSON-LD) means that no single company owns the ecosystem, which could be crucial for industries such as research, supply chain management, or global commerce, where neutrality and interoperability are key. However, because ANP is still bleeding-edge, I'd advise using it in experimental pilots or consortia first, rather than mission-critical production. It's a bet on the future; the upside is huge if a true agent economy takes off, but the path to get there will have hurdles (technical and adoption-wise).
For internal agent workflows within an enterprise, ACP currently stands out as the pragmatic choice. Its design philosophy resonates with CIOs: it's secure, it's controlled, and it plays well with existing infrastructure. In scenarios such as automating cross-departmental processes or connecting AI agents from different business units, ACP provides the oversight and compatibility that enterprises need. IBM's involvement, combined with the Linux Foundation's governance, lends credibility and a roadmap toward standardization, which alleviates long-term concerns. I see ACP (and its implementations, such as BeeAI) becoming the backbone for many corporate AI hubs, much like message-oriented middleware did in the past. Google's A2A is another strong contender here, especially for organizations already invested in Google's AI ecosystem. If your use case demands sophisticated agent cooperation and you want to leverage an industry-wide standard (once it matures), A2A could be worth piloting. It's particularly attractive for multi-agent systems that might extend beyond your organization (e.g., partners or vendors plugging into your workflows) — basically a more open but still semi-centralized alternative. The decision might come down to governance: ACP if you want full control and immediate clarity, A2A if you're banking on a broad industry movement and can handle a bit of uncertainty as the spec evolves.
For secure tool integration and quick wins with AI today, MCP is the low-hanging fruit. If your goal is to empower a single AI assistant with access to your company's internal tools and data immediately, MCP is a proven and straightforward route. It doesn't require overhauling how your systems communicate; instead, it acts as an adaptor. I've seen companies succeed with MCP by quickly prototyping AI-powered assistants that connect to databases, CRMs, or internal APIs — delivering value in weeks, not months. The protocol's limitations (no multi-agent, no memory) aren't deal-breakers in these use cases, because often you just need one AI agent that can fetch information or execute transactions securely on behalf of a user. With optional DID-based authentication, MCP can be configured for secure tool access, ensuring the AI only taps authorized resources. In sectors such as customer support, data analysis, or executive assistance, where an AI agent acting as a savvy tool-user is the immediate need, MCP excels. Just be mindful that as your AI ambitions grow (e.g., you want agents that collaborate), you may eventually need to complement MCP with one of the inter-agent protocols.

In conclusion, no single protocol has yet won the "standard" mantle, and they may coexist, serving different niches. In the near term, I recommend enterprises align protocol choices with their strategic priorities: use MCP to quickly integrate AI into existing operations; experiment with A2A or ACP for orchestrating multiple agents in pilot projects; and keep an eye on ANP if an open-agent network could unlock new business models for you. It's an exciting time in the AI space — akin to the early days of networking — and the choices made now will influence your AI architecture's agility and reach for years to come. Each of these protocols involves trade-offs between control and openness, as well as simplicity and flexibility. The savvy strategy is to start with the protocol that addresses your most pressing needs (tool integration, internal automation, or broad collaboration) while staying adaptive as the ecosystem matures. After all, the only certainty is that AI agents will play an increasingly significant role in business, and communication standards will be the key to unlocking their collective potential.

The Protocol Wars Are Just Beginning!

These four protocols represent the opening moves in what will become the defining infrastructure battle of the AI era. Just as HTTP shaped the internet, the winning agent communication standard will determine how trillions of AI interactions unfold over the next decade.

But here's what most technologists miss: the real competitive advantage isn't in choosing the "right" protocol — it's in staying ahead of the strategic implications as they emerge.

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Written by Dr Hernani Costa and originally published at First AI Movers. Subscribe to the First AI Movers Newsletter for daily, no‑fluff AI business insights, practical and compliant AI playbooks for EU SME leaders. First AI Movers is part of Core Ventures.