A2A vs MCP in Agentic AI

✅ Clarification

A2A refers to Agent-to-Agent communication in multi-agent systems.
MCP refers to the Model Context Protocol, not Master Control Program.

🔁 A2A vs 🧠 MCP (Model Context Protocol)

📘 Definitions

A2A – Agent-to-Agent
A communication pattern where multiple autonomous agents interact directly with one another.

MCP – Model Context Protocol
A structured protocol for framing context for a language model agent. Supports tools, memory, and input/output standardization.

🧠 Conceptual Differences

Aspect	A2A (Agent-to-Agent)	MCP (Model Context Protocol)
Primary Focus	Coordination and interaction between agents	Structuring context and actions for a language model agent
Communication	Peer-to-peer communication among agents	Model is central; protocol governs how it receives and acts on input
Architecture	Typically multi-agent	Typically single-agent, model-mediated
Control Flow	Emergent; agents decide who to talk to and when	Deterministic; orchestrated via MCP message structure
Memory Handling	Distributed or shared memory among agents	Explicit, structured memory fields (in messages like `system`, `user`, `tool`)
Tool Use	Agents call each other's tools or delegate subtasks	Model calls tools via structured MCP `tool_call` messages
Example Use Case	One agent discovers a subtask and asks another agent to handle it	A tool-augmented LLM uses MCP to plan and execute a sequence of actions
Developer Role	Designs agent coordination logic	Constructs well-formed MCP messages

🧠 Who Sponsors or Advocates A2A?

🏢 Organizational Sponsorship

OpenAI is the current leader advocating A2A as an emergent pattern.
Used internally in agentic frameworks, often alongside MCP.

🔑 Key Figures

Andrej Karpathy
Barrett Williams
Jan Leike and Lilian Weng

📚 Academic Roots

Multi-agent systems research from the 1990s onward:
- Michael Wooldridge
- Milind Tambe
- Yoav Shoham

🧠 A2A Competitors in the Agentic AI Mindshare

Competitor Paradigm	Description	Key Players / Tools	Contrasts with A2A
MCP (Model Context Protocol)	Structured protocol to invoke tools, memory, and reasoning in a single agent	OpenAI (MCP)	A2A = multi-agent; MCP = single-agent context orchestration
Toolformer-style Single Agent	A single LLM agent that learns when and how to call external tools	Meta (Toolformer), LangChain	Centralized; no agent-to-agent delegation
Hub-and-Spoke Architectures	Central planner agent delegates to sub-agents; similar to microservices	AutoGen (Microsoft), AgentOps, CrewAI	A2A enables peer-to-peer; hub/spoke assumes central boss-agent
Monolithic Planner	LLM does full chain-of-thought, no delegation; tools are just extensions	OpenAI Assistants API, LangGraph (single thread)	Simpler; lacks modularity and scalability of A2A
Human-in-the-Loop Pipelines	Human coordinates between otherwise isolated agents or tool calls	AI orchestration platforms (Adept, ReAct variants)	A2A can operate autonomously, humans may not be involved
Agent-Swarm Models	Large-scale multi-agent systems that interact via emergent behaviors	ReALM, DERA, certain open-source MAS frameworks	Swarm behavior is less structured; A2A can be more deliberate

🌐 Mermaid Diagram

graph TD
    A["🧠 Single Agent / MCP<br/>- Controlled, structured<br/>- Centralized LLM"]

    A --> B["🧰 Toolformer<br/>- Self-aware tool use"]
    A --> C["🗺️ Monolithic Planner<br/>- Full reasoning without agents"]

    A2A["🤝 A2A (Agent-to-Agent)<br/>- Peer-to-peer<br/>- Autonomous delegation"]
    
    B --> A2A
    C --> A2A

    A2A --> D["🧭 Hub-Spoke<br/>- Planner delegates to sub-agents"]
    A2A --> E["🐝 Swarm / Emergent<br/>- Distributed agents"]

    style A fill:#f9f,stroke:#333,stroke-width:1px
    style A2A fill:#bbf,stroke:#333,stroke-width:1.5px
    style D fill:#bfb,stroke:#333,stroke-width:1px
    style E fill:#dfd,stroke:#333,stroke-width:1px
    style B fill:#ffd,stroke:#333,stroke-width:1px
    style C fill:#ffd,stroke:#333,stroke-width:1px

Generated on: {date.today().isoformat()}

edburns/a2a-vs-mcp-agentic-ai.md