§ Protocols · 15 min read
As AI agents become the new building blocks of enterprise software, understanding how they communicate is crucial. Here's everything you need to know about the five leading protocols shaping the future of multi-agent systems.
Katonic AI
AI Infrastructure Team
The agentic market
projected AI-agents market by 2034
Growth
38.5% CAGR · from $5.9B in 2024
The AI landscape is witnessing a fundamental shift. As enterprises move from single AI models to sophisticated multi-agent systems, the question of how these agents communicate with each other - and with external tools - has become the defining challenge of 2025-2026. Five protocols have emerged as frontrunners: MCP, A2A, ANP, ACP, and AGORA.
According to recent research, 78% of global organisations already use AI tools in their daily operations, and 85% have integrated agents into at least one workflow. The global AI-agents market, valued at $5.9 billion in 2024, is projected to reach $105.6 billion by 2034 - a staggering 38.5% compound annual growth rate. Understanding which protocol to use (and when) is no longer optional - it's a strategic imperative.
These protocols aren't competing standards - they're complementary building blocks that address different layers of the AI integration stack. Understanding when to use MCP, A2A, ANP, ACP, or AGORA (or a combination) is critical for building robust, scalable agentic systems.
Before diving deep into each protocol, let's understand their fundamental positioning. Each protocol solves a different part of the communication problem - some focus on tool integration, others on agent-to-agent collaboration, and some on decentralised networks.
Model Context Protocol
Anthropic · Late 2024
The “USB-C port for AI” - standardises how AI models connect to external tools, data sources, and APIs. Think of it as giving your LLM hands to interact with the world.
Agent-to-Agent Protocol
Google · April 2025
Enables AI agents to discover, communicate, and collaborate across vendors and platforms. Designed for enterprise-scale task delegation and workflow coordination.
Agent Network Protocol
Open Source · 2024
The “HTTP of the Agentic Web” - a decentralised protocol using W3C DID standards for secure, peer-to-peer agent communication without central authorities.
Agent Communication Protocol
IBM · March 2025
REST-native messaging with multimodal support and flexible session management. Powers IBM's BeeAI platform for enterprise multi-agent workflows. Now merging with A2A.
Agora Protocol
Research · 2024
A meta-protocol that uses natural language for rare communications and structured routines for frequent ones. Enables self-organising agent networks without human intervention.
The following table provides a comprehensive comparison across key dimensions that matter for enterprise AI deployment:
| Aspect | MCP | A2A | ANP | ACP | AGORA |
|---|---|---|---|---|---|
| Focus Area | Context/tool integration between AI models and external systems | Direct peer-to-peer task delegation and negotiation | Large-scale decentralised agent network coordination | Modular, extensible messaging & session framework | Natural language-driven agent interaction and orchestration |
| Communication | Request-response via JSON-RPC | Async task messaging (HTTP/2, HTTP/3, SSE, webhooks) | Gossip-based broadcast, negotiation, discovery | Message queues, brokers, streaming (REST, multipart MIME) | LLM-mediated dialogue using NL instructions |
| Security | Basic authentication, TLS optional | TLS over HTTP/2 or HTTP/3 with capability cards | Decentralised PKI (DPKI), DIDs, zero-trust | Centralised ACLs, broker auth, role-based access | Implicit trust; depends on LLM security hardening |
| Scalability | Small- to medium-scale integrations | Moderately scalable with some central coordination | Internet-scale decentralised discovery | Scales well with brokers and load balancing | Flexible, but limited by LLM scaling & cost |
| Fault Tolerance | Low, central server dependency | Medium; fallback via peer reconnection | High fault tolerance via peer redundancy | Medium-high; broker clustering supported | Emergent; depends on redundancy of LLM backends |
| Latency | Low (direct client-server) | Medium (peer lookup & negotiation overhead) | Variable; higher for multi-hop broadcasts | Low if broker proximity is good | Variable; depends on LLM inference speed |
| Best Use Case | Tool use, RAG pipelines, AI assistants needing structured context | Multi-agent workflows, delegation, marketplace tasks | Decentralised IoT or “agentic web” ecosystems | Enterprise multi-modal workflows, reliable messaging | High-level orchestration, negotiation via natural language |
| Protocol | JSON-RPC over HTTP(S) | HTTP/REST with QUIC, SSE | JSON-LD, DIDs, peer-to-peer overlays | REST + message queues (MQTT, AMQP, Kafka-like) | Natural language + LLM understanding, not rigidly standardised |
When it comes to agent communication and coordination, each protocol has a unique role in the ecosystem. Let's examine each one's focus, ideal use cases, strengths, and limitations in detail.
The Model Context Protocol, launched by Anthropic in late 2024, has quickly become the standard for connecting AI models to external tools and data sources. Think of MCP as the “USB-C port” for AI - a universal connector that allows any AI model to interact with databases, APIs, file systems, and more.
MCP operates on a client-server architecture where the AI model (client) connects to MCP servers that expose tools, resources, and prompts. The protocol handles all the complexity of tool discovery, invocation, and result parsing, allowing developers to focus on building capabilities rather than integration plumbing.
“MCP fixes what experts call the ‘M×N integration problem’ - the complex task of connecting many AI models to various tools or data sources. With MCP, you build one integration per tool, and it works with any MCP-compatible AI system.”
MCP excels at structured tool invocation, context management, and maintaining auditability. It's the right choice when you're building a single AI assistant that needs to access databases, execute code, or interact with external APIs - but you're not yet dealing with multi-agent orchestration.
Google's Agent-to-Agent Protocol, announced in April 2025, addresses a different layer of the stack - how autonomous agents discover each other, delegate tasks, and collaborate on complex workflows. A2A is intentionally stateful, designed for long-running, multi-step tasks that span multiple agents.
The protocol uses “Agent Cards” - JSON metadata documents that describe an agent's capabilities, skills, and contact information. This allows agents to dynamically find and interact with other agents in the ecosystem, creating what Google hopes will be an “app store moment” for AI developers.
A2A excels when you need specialised agents from different vendors to interact naturally. It supports enterprise-scale workflows with clear task lifecycles (“submitted”, “working”, “input-required”, “completed”), multipart messages, and flexible communication channels. The protocol is backed by over 50 tech giants including Salesforce, Accenture, and SAP.
The Agent Network Protocol aims to become the “HTTP of the Agentic Web era.” Unlike centralised approaches, ANP enables agents to establish encrypted communication connections directly, using W3C Decentralised Identifiers (DIDs) for authentication without relying on any central authority.
ANP's three-layer architecture includes: an Identity Layer for decentralised authentication, a Meta-Protocol Layer for dynamic protocol negotiation, and an Application Layer for semantic-based capability descriptions. This structure enables truly decentralised agent marketplaces and AI-native web interaction.
ANP is ideal for scenarios requiring high security without a unified trust centre - such as cross-platform personal data integration, IoT device agent negotiation, or research agent communities where agents from different teams need to interact according to open protocols. The W3C AI Agent Protocol Community Group is now working to standardise ANP-based approaches.
IBM's Agent Communication Protocol was launched in March 2025 to power its BeeAI Platform. Built on REST principles with HTTP-native endpoints, ACP is designed for seamless integration with existing enterprise infrastructure. Its SDK-optional architecture means you can test agent interactions with simple curl commands or Postman.
One of ACP's distinguishing features is its multimodal message support - messages can contain structured data, plain text, images, or embeddings, making it suitable for complex workflows involving LLMs, vision models, or hybrid systems. ACP also supports both synchronous and asynchronous messaging patterns.
Important update: As of August 2025, ACP has merged with A2A under the Linux Foundation umbrella. This consolidation aims to accelerate progress toward a single, more powerful standard for agent communication. The BeeAI platform now uses A2A, and migration paths are available for existing ACP users.
AGORA represents a fundamentally different approach to agent communication. Rather than prescribing fixed message formats, it leverages LLMs' natural language understanding to enable flexible, adaptive communication between agents.
The protocol solves what its creators call the “Agent Communication Trilemma” - the challenge of achieving versatility (handling varied use cases), efficiency (minimal computational effort), and portability (minimal human setup effort) simultaneously. AGORA sidesteps this by using standardised routines for frequent communications, natural language for rare communications, and LLM-written routines for everything in between.
AGORA enables self-organising, fully automated protocols that achieve complex goals without human intervention. On large AGORA networks, researchers have observed the emergence of agents that autonomously develop coordination strategies - a glimpse of what truly autonomous multi-agent systems might look like.
Don't confuse the AGORA protocol (a research meta-protocol for LLM agent communication) with Agora.io (a commercial real-time communication platform for voice/video). While Agora.io excels at ultra-low latency, end-to-end encrypted media streaming for video conferencing, live streaming, and interactive apps, the AGORA protocol discussed here is specifically designed for AI agent coordination using natural language negotiation.
Each protocol serves a different layer of the agent ecosystem. Choosing the right one depends on whether you need context (MCP), autonomy (A2A), scale (ANP), compliance (ACP), or emergent coordination (AGORA).
An IDE-integrated assistant that needs to read files, execute code, query databases, and interact with version control systems.
A support request triggers collaboration between chatbots, billing systems, inventory databases, and knowledge base agents - invisible to end users.
Personal AI assistants directly communicating with third-party service agents across platforms, with cryptographic identity verification.
A purchase order flows from orchestration bot to policy-checking agent to reporting agent, mixing vendor and custom agents seamlessly.
A network of specialised research agents that autonomously negotiate protocols and coordinate on complex analytical tasks.
AI mechanics use MCP for diagnostic tools and repair manuals, while using A2A to communicate with parts suppliers and billing agents.
The most sophisticated enterprise deployments will use multiple protocols together. Think of it as a layered architecture:
A typical enterprise deployment might use ACP for local real-time coordination, A2A for cloud-based orchestration, MCP for tool access, and ANP for cross-organisational agent networks - each protocol handling what it does best.
The Katonic approach
At Katonic AI, we've built our platform to embrace this multi-protocol future. Our MCP Gateway supports 240+ verified MCP servers, while our agent orchestration layer can coordinate agents regardless of their underlying communication protocol.
The decision of which protocol to use isn't about picking a winner - it's about understanding which layer of the integration stack you're working on:
As the protocols mature and consolidate (like the ACP-A2A merger), expect clearer standards to emerge. The good news? Building on any of these open standards protects you from vendor lock-in and positions you well for the multi-agent future.
Industry analysts argue that multi-agent systems could become the next layer of IT infrastructure. Standards like A2A and MCP are emerging as the “HTTP of agents,” providing common ground for discovery, messaging, and security. Organisations that invest in understanding these protocols now will be best positioned to leverage the agentic revolution.
Katonic AI provides enterprise-grade AI agent platforms that help organisations deploy, configure, and scale AI agents rapidly. Our MCP Gateway and Agent Marketplace offer pre-built integrations that work with any protocol standard, all running on infrastructure you control with full governance, security, and data sovereignty.
To explore how Katonic supports the multi-protocol agent future, visit katonic.ai
Katonic AI
AI Infrastructure Team
Katonic AI provides enterprise-grade AI agent platforms that help organisations deploy, configure, and scale AI agents rapidly. Our MCP Gateway and Agent Marketplace offer pre-built integrations that work with any protocol standard.
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