MCP and ACP are distinct yet complementary protocols designed to address different challenges in AI development, particularly concerning model enrichment and multi-agent system scaling. MCP focuses on providing external context to individual AI models, enhancing their decision-making by integrating external data or capabilities. In contrast, ACP facilitates communication, collaboration, and resource sharing among autonomous agents in distributed systems. While MCP is about "giving context to a model," ACP is about "letting agents collaborate at scale." The ultimate choice and combination of these protocols depend on whether the goal is to "enrich a model" (MCP) or "orchestrate agents" (ACP).
1. Distinct Purposes
The fundamental distinction lies in their primary objectives:
Model Context Protocol (MCP): "MCP is tailored for enriching individual AI models with external context (data or agents) to enhance their decision-making and response generation." It's about "adding context easily and is primarily useful when an AI model needs access to external data sources not under your control."
Agent Connect Protocol (ACP): "ACP, on the other hand, enables autonomous agents to collaborate and share resources in a distributed system." Its focus is on "agent communication and collaboration, ensuring that agents can interact and solve problems cooperatively."
2. "Containment" vs. "Messaging" Analogy
The source uses a programming analogy to clarify the difference:
MCP (Containment): In MCP, "a model can be augmented with external context, which could include data or capabilities from another system (or potentially another agent)." This external context is like an "attribute" that enhances the agent's behavior, "similar to how an agent in MCP 'contains' (albeit remotely) the context or capabilities of another agent." The interaction is a "direct client server interface."
ACP (Messaging): "With ACP agents exchange messages via restful APIs to produce a result." This is akin to how "programming uses message calls, where objects communicate by invoking methods on each other."
3. Use Cases and When to Apply
The document provides clear guidance on when to use each protocol:
Use MCP when:Building an agent where you don't control the model or tools and need a protocol to connect them.
An AI model "needs access to external data sources not under your control."
An agent (AgentA) needs information from another agent (AgentB), and AgentB is "purely a source of information for AgentA to use."
You need to "build individual agents that require tight integration with (and repeated access to) external data sources."
You are creating "deeply integrated stateful agents."
Use ACP when:You have "built agents with defined purposes and want them to interoperate at scale."
You are "building a system of agents and you don't control them."
"AgentA and AgentB, collaborate and reason together."
You need to "orchestrate and scale interaction between agents built with MCP."
You need to "enable these agents to scale, communicate, and be reused effectively."
4. Relationship between Agents
The nature of interaction between agents differs:
MCP: If agents are involved in MCP, "the call path is a tool call which limits the relationship between agents." Communication between agents is possible "as a tool call."
ACP: The "relationship between agents is not limited to tool calling." Communication between agents is "as a peer call."
5. Microservices Analogy for Scaling Agents
The source strongly advocates for applying microservice principles to agent-based systems, especially with ACP:
Agents as Microservices: "As AI agent ecosystems grow, we’ll need strategies for deploying, reusing, and scaling agents effectively. The focus will shift from the individual agent to how agents can be composed and reused at the task level—the “Job to Be Done” (JTBD) reuse level."
Key Principles:Loose Coupling: Agents should "interact via well-defined protocols, minimizing dependencies and maximizing flexibility." ACP "naturally supports loose coupling by enabling message passing between agents."
High Cohesion: "Each agent is self-contained and focused on a single function, making it easier to deploy, scale, and reuse." ACP "promotes high cohesion by allowing multiple agents to communicate as a logical unit to achieve a JTBD." MCP, conversely, "tightly couples remote information with an agent’s internal model, requiring persistent state maintenance," leading to "Lower cohesion."
State Management: With ACP, "State is maintained inside the agent, enabling flexible scaling." With MCP, "State must be synchronized across data sources, making scaling harder."
6. Integration Benefits and Synergistic Use
MCP and ACP are not mutually exclusive; they can be used together for more robust systems:
Enhanced Data Access: "MCP can provide AI models with context from various data sources, while ACP can facilitate communication between agents, allowing them to share and utilize this context effectively."
Improved Collaboration: "ACP enables agents to collaborate and negotiate tasks, and MCP can supply the necessary data and context to make these interactions more informed and efficient."
Unified Framework: "Using MCP for data integration and ACP for agent communication creates loosely coupled where AI models and agents can operate seamlessly, leveraging both protocols' strengths."
Powerful, Modular AI Architectures: "When used together, these protocols create powerful, modular AI architectures capable of scaling."
7. Technical Implementation Details
MCP: "At its core gives large language models (LLMs) and agents access to prompts, resources, and tools in a standardized way." These services are listed within an MCP server, and "MCP clients (LLM, agents) can then search for and consume these resources as needed via the MCP protocol."
ACP: "Defines an interface, in the form of REST endpoints, that define how to interact with agents in a standardized way." It defines endpoints for agentic workflows, context threads, and running the agent. It is "primarily focused on standardized multi-agent interactions which preserve state context via threads." An ACP-implementing agent "could also be an MCP client/host to connect to data."
Conclusion
The source emphasizes that "the difference between 'this works' and 'this scales like a beast' comes down to choosing the right protocol for the job." MCP excels at deepening the capabilities of individual models by providing rich external context, forming the foundation of stateful agents. ACP, on the other hand, is the key to unlocking scalable, collaborative, and reusable multi-agent systems by enabling loose coupling and effective communication. By strategically combining these protocols, developers can build "agent-based architectures at scale, following the same principles that make microservices successful in cloud computing," leading to the "Internet of Agents."
FAQ for those who are preparing for an Interview
What are MCP and ACP, and what are their primary purposes?
MCP (Model Context Protocol) and ACP (Agent Connect Protocol) are distinct protocols designed for different aspects of AI system development. MCP's primary purpose is to enrich individual AI models by providing them with external context, such as data or capabilities from other systems. It focuses on integrating external information to enhance a model's decision-making and response generation. ACP, on the other hand, is designed to enable autonomous agents to communicate, collaborate, and share resources within a distributed system. Its focus is on facilitating inter-agent interaction and problem-solving at scale.
How do MCP and ACP differ in terms of their core functionalities?
The core difference lies in their focus: MCP is about "containment," meaning it augments a model by having it "contain" (access) external context or capabilities, often through a client-server interface where the external content is an "attribute" of the agent. ACP, however, is about "messaging," where agents exchange messages via RESTful APIs to collaborate and produce results. MCP enhances a single model's capabilities by providing it with external data, whereas ACP facilitates collaborative interactions and resource sharing among multiple independent agents.
When should one choose to use MCP versus ACP?
The choice between MCP and ACP depends on the specific needs of the AI system being built.
Use MCP when you are building an agent (or a very contained multi-agent system) and you need to provide an AI model with external context or tools that are not under your direct control. It's ideal for enhancing the performance of individual models by giving them standardized access to external prompts, resources, and tools.
Use ACP when you have built autonomous agents with defined purposes and you need them to interoperate, collaborate, and scale effectively in a distributed system. It's designed for scenarios where agents need to share resources and engage in complex, state-preserving interactions.
Can MCP and ACP be used together, and what are the benefits of their integration?
Yes, MCP and ACP can be used together to create powerful and modular AI architectures. The integration benefits include:
Enhanced data access: MCP can provide AI models with context from various data sources, while ACP facilitates communication between agents, allowing them to effectively share and utilize this context.
Improved collaboration: ACP enables agents to collaborate and negotiate tasks, and MCP can supply the necessary data and context to make these interactions more informed and efficient.
Unified framework: Using MCP for data integration and ACP for agent communication creates a loosely coupled system where AI models and agents can operate seamlessly, leveraging the strengths of both protocols.
How do MCP and ACP relate to the concept of microservices in software architecture?
ACP aligns closely with microservice architecture principles. It promotes the idea of agents as self-contained microservices that manage their own state and interact through well-defined APIs. This loose coupling ensures scalability, maintainability, and resilience, similar to how microservices are deployed. MCP, on the other hand, introduces a tighter coupling by integrating remote information directly with an agent's internal model, which can expose the state of remote data and make scaling more challenging if used in isolation for agent reuse.
What are "loose coupling" and "high cohesion" in the context of AI agents, and how do MCP and ACP support them?
Loose coupling means that agents interact via well-defined protocols, minimizing dependencies and maximizing flexibility, so changes in one agent have minimal impact on others. ACP naturally supports loose coupling by enabling message passing between agents, with each agent maintaining its own state.
High cohesion means that an agent is self-contained and focused on a single function, making it easier to deploy, scale, and reuse. ACP promotes high cohesion by allowing multiple agents to communicate as a logical unit to achieve a "Job to Be Done" (JTBD). MCP, conversely, can lead to lower cohesion as it tightly couples remote information with an agent’s internal model, potentially exposing remote data state during reuse.
How do MCP and ACP impact the scalability and reusability of AI agents?
Scalability: ACP is designed for scalable agent interactions. It enables agents to be deployed and reused like microservices because they are self-contained and communicate via messages, maintaining state internally. MCP, if used alone for agent reuse, can make scaling harder due to its tight coupling with remote data sources and the need to synchronize state across them.
Reusability: ACP promotes high reusability by allowing agents to be highly cohesive and encapsulated, making it easier to combine them into single logical units for specific tasks. This aligns with the "Job to Be Done" (JTBD) reuse level. MCP, by tightly coupling remote information, can make agent reuse more complex due to persistent state dependencies and exposed remote data state.
In a practical deployment scenario, how should one strategically choose between MCP and ACP for optimal performance?
For optimal performance and scalability:
Use MCP for building individual agents that require deep integration with and repeated access to external data sources. These agents form the foundation and are designed for tight data integration.
Use ACP to orchestrate, scale, and enable communication between agents, especially those built with MCP. ACP allows agents to encapsulate their state, be composed efficiently at the JTBD level, and behave like microservices, ensuring scalability and reusability in large-scale agent ecosystems. The ultimate strategy involves using MCP to create deeply integrated, stateful agents and then using ACP to enable these agents to scale, communicate, and be reused effectively within a larger architecture.