What Every AI Engineer Should Know About A2A, MCP & ACP

Introduction

• Topic: AI protocols that enable agents to communicate, think, and collaborate.
• Protocols Discussed: MCP (Model Context Protocol), ACP (Agent Communication Protocol), A2A (Agent-to-Agent Protocol).

MCP (Model Context Protocol)

• Introduced by: Anthropic
• Purpose: Standardized interface for providing context to large language models (LLMs).
• Core Functionalities:
  • Contextual Data Injection: Import external resources into prompts or memory.
  • Function Routing & Invocation: Models can dynamically invoke tools without hardcoding.
  • Prompt Orchestration: Assemble context dynamically to optimize prompts.
• Implementation:
  • Operates over HTTP(S) with JSON descriptors.
  • Model-agnostic, compatible with API gateways and authentication standards.
• Use Cases:
  • LLM integration with internal APIs for secure data access.
  • Enterprise agent context provision.
  • Dynamic prompt tailoring.

ACP (Agent Communication Protocol)

• Proposed by: BeeAI and IBM
• Purpose: Enables structured communication between AI agents in local environments.
• Design & Architecture:
  • Decentralized agent environment.
  • Event-driven messaging and local discovery.
  • Optional runtime controllers for orchestration.
• Implementation:
  • Designed for low-latency, local-first environments.
  • Compatible with gRPC, ZeroMQ.
  • Emphasizes local sovereignty.
• Use Cases:
  • Multi-agent orchestration on edge devices.
  • Local-first LLM systems.
  • Autonomous runtime environments.

A2A (Agent-to-Agent Protocol)

• Introduced by: Google
• Purpose: Cross-platform communication among AI agents.
• Protocol Overview:
  • HTTP-based communication model.
  • Agents expose an Agent Card with details like capabilities.
• Core Components:
  • Agent Cards: Description of capabilities and endpoints.
  • A2A Client/Server Interface: Enables dynamic task routing.
  • Message & Artifact Exchange.
  • User Experience Negotiation.
• Security: OAuth 2.0, API key authorization, capability-scoped endpoints.
• Use Cases:
  • Cross-platform agent ecosystems.
  • Distributed agent orchestration.
  • Multi-agent collaboration frameworks.

Protocols Compared

• A2A + MCP: Complementary, not competitive. MCP integrates AI with tools; A2A connects AI agents.
• ACP's Role: Focus on local, low-latency coordination without cloud dependency.

Future Outlook

• Convergence vs. Fragmentation:
  • Convergence: Unified platforms using all protocols optimally.
  • Fragmentation: Divergence into incompatible systems.
  • Middle Ground: Use of open-source tools to bridge differences.

Conclusion

• Building and adopting these protocols now will determine the cohesion of future AI ecosystems.
• Companies like Addepto offer expertise in deploying these protocols effectively.

This summary provides a comprehensive overview of AI communication protocols and their unique roles in facilitating robust, modular, and efficient AI systems across different environments and use cases.