Unlocking the Power of Multi-Agent AI Systems: The Top 5 Open Protocols

As multi-agent AI systems become a foundational layer of enterprise tech stacks, the focus is shifting from optimizing standalone models to enabling intelligent agents to think, communicate, and act collaboratively. This trend is already underway. Accenture finds that one‑third of large enterprises already deploy AI agents to boost innovation [1], and Gartner recently predicted that by 2028, a third of enterprise apps will embed agentic AI, with 15% of routine decisions made autonomously. [2]

New infrastructure is emerging to support this transition. While Model Context Protocol (MCP) is gaining traction for the right reasons, it’s just one aspect of the larger ecosystem. For those looking to build scalable multi-agent AI systems, it’s essential to understand five key protocols: MCP (Model Context Protocol), ACP (Agent Communication Protocol), A2A (Agent-to-Agent Protocol), ANP (Agent Network Protocol), and AG-UI (Agent-User Interaction Protocol). These protocols define how AI agents interact, function independently, and collaborate. Explore how they are shaping smarter, more efficient AI ecosystems.

Top 5 Open Protocols for Multi-Agent AI Systems

Think of your AI system as a large-scale organization, where each AI agent acts as a dedicated employee or function. For the organization to thrive, it needs a strong structure, clear communication, seamless coordination, accessible resources, and intuitive interfaces.

• MCP (Model Context Protocol) is the internal wiki and playbook, which provides access to the organization’s internal tools. Therefore, each employee consults these resources to learn how to perform their job, where to obtain information, and which tools they can collectively utilize.

• ACP (Agent Communication Protocol) is comparable to an organization’s communications systems, such as Slack, email, and Jira. Whether it entails quick updates, formal requests, or task assignments, ACP ensures everybody can communicate clearly across functions, teams, and tools.

• A2A (Agent-to-Agent Protocol) would be two functions (Sales and Finance, for example) collaborating without any management oversight, allowing them to work together on complex, multi-step processes. 

• ANP (Agent Network Protocol) resembles HR directory and procurement systems, where you can find colleagues, check their roles, verify identities, and connect with them securely. 

• AG-UI(Agent-User Interaction Protocol) serves as the front-end interface, similar to an internal dashboard or workflow tool, where employees view tasks, enter data, or control processes. It ensures real-time visibility and interactions. 

Figure 1: Key Protocols for Building Agentic AI Systems

MCP (Model Context Protocol): The Standard Translator for AI Context

The Model Context Protocol (MCP) is increasingly recognized for its ability to provide structured contexts, such as tools, datasets, or prompts, to large language models (LLMs).

Without MCP, each app or data source requires custom integration per LLM that relies on external context to function. Doesn’t that sound like a maintenance nightmare? Consider MCP to be the AI equivalent of USB-C: one ubiquitous standard that seamlessly functions. Instead of building one-off connectors every time, MCP provides a plug-and-play, reusable framework. Envision it as writing something once and then using it anywhere you like. [3]

Key features:

• Universal Tool Connection: Standardizes the connection between AI models and external tools and APIs.
• Data Source Integration: Allows access to live information and databases.
• Context Awareness: Helps ensure AI has accurate and up-to-date data.
• Secure Communication: Provides methods to verify and manage permissions. 

How MCP works:

• An AI agent receives a task that requires external knowledge.
• The agent identifies an MCP-compatible tool that can help fulfill the request.
• The agent requests permission to use the tool.
• MCP acts as a tool to invoke external data, returning output in a standardized format. 
• The agent utilizes this information to give the final response to the user. 

Figure 2: Benefits of MCPs

We have incorporated MCP support within our Generative Studio X (GSX), which offers a simple, no-code solution for integrating external MCP servers directly into your AI agents. You can easily add, edit, and manage connections with immediate validation. 

Example use case:

An employee keeps getting disconnected from their VPN. They reach out to an AI agent for resolution. The agent recognizes that the issue needs diagnostic data and uses MCP to access a compatible IT monitoring tool. It requests permissions, asks for the external tool to provide information, and receives standardized output indicating the issue. Using this information, the AI agent then explains the problem to the employee and suggests steps to fix it. It all happens in real-time without any manual IT intervention.

What are Agentic AI Communication Protocols?

How do businesses work? People talk, share information, collaborate on projects, and coordinate across departments. AI agents need to do the same things but at scale and much faster. That’s where agentic AI protocols come in. They’re the rulebook that enables AI agents to communicate, understand one another, and work together seamlessly.

Without these protocols, AI systems would be stuck talking to themselves, unable to collaborate. Agent communication protocols standardize how messages flow between agents, ensuring they understand each other.

AI interoperability becomes possible when these protocols function together: agents from different vendors can collaborate. A2A (agent-to-agent) communication allows agents to negotiate directly without needing a central “boss” to manage every interaction. This kind of messaging makes workflows fluid and intelligent.

When you implement agentic AI protocols properly, you’re not just connecting systems, you’re creating an intelligent ecosystem where AI agents become true collaborators, making your enterprise faster, smarter, and more resilient.

ACP (Agent Communication Protocol): Enabling Communication Between AI Agents

Even the most intelligent AI agent will fail to achieve much if it can’t communicate clearly with people, apps, or any other systems. The Agent Communication Protocol (ACP) addresses this by standardizing messaging formats across various users, including agents, applications, and users.

Built on a RESTful API structure and MIME-type extensibility, ACP is designed to support diverse message formats and is flexible enough to work across any technology stack, whether it’s Python, Java, or another platform.

Key features:

• Workflow Orchestration: Ensures smooth coordination as agents collaborate to achieve a common goal.
• Reliable Task Delegation: Task assignments and outcomes are standardized.
• Context Management: Maintains context throughout a series of interactions.
• Observability: Provides tools to monitor and audit agent behavior.

How ACP works:

• An orchestrator agent creates a task and delegates it to specialized agents using ACP’s messaging format. 
• Agents receive the tasks and communicate progress and results using ACP’s structured messaging.
• Agents can pause or escalate tasks to request additional inputs or trigger human-in-the-loop actions. 
• Results flow back to the orchestrator or are passed along the workflow chain. 
• The entire process is auditable with observability hooks.

Benefits:

One of the benefits of ACP is that it enables communication across platforms and programming languages, overcoming vendor lock-in. Its flexible design supports various messaging types like tasks, updates, queries, and alerts. Major industry players, such as IBM and the Linux Foundation, are actively working towards developing open and secure AI systems. [4] A recent report also highlighted that an ACP-powered system has achieved 28.3% accuracy in complex, long-horizon tasks, underscoring its robustness and extensibility. [5]

Example use case:

In a customer support center, the ACP protocol allows AI agents, human agents, and backend systems to exchange structured messages. These messages can include ticket updates, customer intentions, or task handoffs across various platforms. The protocol facilitates smooth asynchronous communication, ensuring all agents remain informed regardless of the tools or timelines they are using.

A2A: Agent-to-Agent Protocol for Deep Collaboration

Enterprise AI is becoming increasingly collaborative, and the Agent-to-Agent Protocol (A2A) enables your AI systems to work together as a cohesive team. A2A, built on HTTP and JSON-RPC with robust security, supports extended interactions between AI agents from different platforms that can track their state.

“A2A has the potential to unlock a new era of agent interoperability, fostering innovation and creating more powerful and versatile agentic systems”, said Google in its announcement of the A2A protocol. [6]

Key features:

• Identifying Other Agents: Agents can find other agents with specific skills or knowledge.
• Capability Sharing: Agents advertise their capabilities via “Agent Cards.”
• Communication: The way agents ask for information and provide it is usually standardized.
• Experience Coordination: Agents can develop plans on how to present themselves effectively to users, as well as how to interact with each other optimally.

How A2A works:

• In short, the public ‘Agent Card’ is how agents market their services and provide information on how to contact them.
• Other agents can be made aware of these capabilities and open the communication.
• The messages transmitted back and forth between the agents are standardized.
• When a task arises that requires specialized capabilities, it can be assigned to one of these agents.

Benefits:

A2A enables AI agents to collaborate in real-time, directly, and without the need for a central orchestrator. It provides support for long-running, stateful workflows, allowing agents to retain context between multi-step tasks. It supports safe, multimodal communication (text, audio, video) as well as cross-vendor interoperability, enabling easy and trusted collaboration across disparate environments and systems.

Example use case:

Consider an online shop. Different AI agents perform various tasks, such as checking stock levels, processing payments, detecting fraud, and coordinating deliveries.

Using A2A (a method of connecting AI agents to enable direct communication between them), the agents can work together more effectively and efficiently. Once a customer has placed an order, the payment agent will charge their account immediately, while the fraud detection agent verifies the transaction’s legitimacy. Meanwhile, the logistics agent can arrange delivery, all of which is done automatically and instantaneously. This decentralized agent-to-agent collaboration streamlines the entire process, making it more reliable and easily scalable across regions, platforms, and suppliers.

ANP (Agent Network Protocol): Building the Internet of Agents

What if AI agents can discover and collaborate securely with each other on a larger scale? ANP makes that possible. Unlike A2A, intended for direct, real-time communication and task-based collaboration between agents, ANP handles how agents help in discovering, identifying, and securely connecting with agents across networks and organizations. [7]

Key features:

• Three-layer architecture.Decentralized identity & secure E2E messaging. 
• Meta-protocols for communication negotiation.
• Application layer for capability registration & discovery.
• Supports trusted agent interaction in distributed systems.

How ANP works: 

• Agent Discovery: To locate and identify other agents across networks or systems.
• Decentralized Identity: Each agent has a verifiable ID (usually using DID standards) to ensure secure access.
• Secure Communication: Enables encrypted, end-to-end messaging between agents, regardless of their organizational affiliation or the platform they use.
• Capability Registration: Agents advertise their capabilities, allowing others to find and invoke services or actions.
• Cross-Network Collaboration: Enables and supports the collaboration of agents operating in different distributed network environments, such as other clouds or multi-tenant systems, facilitating such collaborations.

Benefits:

The ANP enables AI agents to discover and identify one another in the network, facilitating seamless collaboration across organizations, platforms, and cloud environments. Each agent has a unique, verifiable identity, ensuring secure and trusted end-to-end communication. 

Example use case

Imagine a scenario where AI agents can interoperate across the global supply chain, including suppliers, manufacturers, and delivery partners. Using ANP, these AI agents can locate one another and securely authenticate their identities to exchange critical information. This enables the agents to synchronize inventory and plan production and delivery routing without any central oversight or human intervention.

AG-UI (Agent-User Interaction Protocol): Making AI Human-Friendly

All techs, regardless of how advanced, are designed with user experience in mind, specifically in how they interact with humans. Think of it as a universal translator for AI-driven systems — no matter what language an agent speaks, AG-UI ensures fluent communication.

This is where the AG-UI protocol comes in. It helps developers build next-generation agentic workflows that need real-time interactivity, live state streaming, and human-in-the-loop collaboration. [8]

Key features:

• Event-Driven Architecture: AG-UI protocols are designed to respond to client-side or agent-side events, which trigger updates or responses.
• Standardized Agent Event Types: This outlines common interaction events such as receiving a message, starting a task, or sending an update across different user interfaces.
• Supports Bidirectional Interaction: The protocol enables bidirectional interaction between users and agents, maintaining an interactive loop.
• Streams real-time agent updates: UI frameworks using AG‑UI commonly rely on SSE (Server-Sent Events) or WebSockets to push real-time updates from agents. This way, users do not need to refresh or poll for status; instead, agents can share partial outputs, progress updates, or results as they are generated, providing fast, dynamic, and responsive experiences.

How it works:

• UI subscribes to AI agent event streams.
• Agents generate events in response to user input or system triggers.
• Events are delivered instantly for dynamic UI updates.
• Users can respond or take action, with their inputs flowing back to the agent.

Benefits:

The AG-UI protocol generates a seamless and responsive user experience by allowing real-time, two-way interaction between AI agents and users. It ensures consistency in communication across platforms, whether on the web, mobile, or messaging apps by standardizing cross-platform interaction. 

Example use case 

Consider a user engaging with an AI financial adviser in preparation for buying a home. The AG-UI enables the AI agent to provide real-time recommendations, pose follow-up questions, and adjust advice on the fly, making the process more lively and engaging, as if the user is interacting with a human agent.

Figure 3: The Agent Protocol Stack

Source: Dev.to

From Protocol to Practice: Building the Infrastructure for Scalable AI Ecosystems

We’re entering what we call a “protocol-centric” phase of enterprise AI. As systems evolve, it won’t soon matter which model you choose, but rather how well your AI ecosystem communicates, collaborates, and coordinates with each other. 

For enterprise AI to reach its next horizon of protocol-focused adoption, success will depend on the interconnectivity, coordination, and scaling capabilities of your systems. The five open protocols we’ve outlined— MCP, ACP, A2A, ANP, and AG-UI — are the foundational building blocks for operationalizing AI agents at scale with flexibility, resilience, and interoperability across teams, tools, and touchpoints.

If you’re committed to deriving real business value from AI agents, it’s time to design systems that foster openness. At OneReach.ai, we have developed Generative Studio X (GSX) to enable enterprises to implement these protocols using no-code tools and a secure cloud.

Related Questions About Open Protocols

1. What makes these protocols “open”?

Open protocols are vendor-neutral, publicly documented, and interoperable across systems. They enable AI agents, tools, and platforms to work together seamlessly without proprietary lock-in, making them ideal for large-scale, enterprise-grade AI architectures.

2. Do I need to implement all five protocols to build an agentic AI system?

Not always. Each protocol solves a specific challenge — Every one of them addresses a different problem: MCP deals with context, ACP with communication, A2A with collaboration, ANP with recognition, and AG-UI with human interaction. You could begin with just one or two, depending on your use case, and then integrate more as your system matures.

3. How do these protocols improve integration and reduce costs?

These processes avoid costly custom integration by employing standard formats and communication models. This reduces engineering efforts, deployment time, and costs, and is particularly useful in large, multi-agent worlds where maintenance costs are a leading concern.

4. Are these protocols already used in production environments?

Correct. MCP, ACP, and A2A are being utilized in live implementations in the healthcare, finance, and retail industries. These standards are being actively engaged with, or are being developed and incorporated by, larger actors such as Microsoft, Google, and IBM.