Architecting Intelligent Multi-Agent AI Systems: A2A vs MCP

Author(s): Adit Sheth

Originally published on Towards AI.

As AI continues to evolve beyond single-model interactions, we are witnessing a profound transformation in how intelligent systems are designed, built, and deployed. Enterprises are no longer content with standalone LLM-driven tools. Instead, they are embracing multi-agent systems — ecosystems of autonomous AI agents that collaborate to solve complex, high-value tasks.

This shift demands more than just smarter models. It requires robust communication protocols, architectural scalability, memory management, orchestration, and a rethinking of how AI agents talk to one another.

Two architectural approaches dominate this landscape: Agent-to-Agent (A2A) protocols and the Model Context Protocol (MCP). Each enables AI agents to collaborate — but in profoundly different ways. In this article, we dive deep into their architectures, technical trade-offs, use cases, and future implications for enterprise AI design.

The Rise of Multi-Agent Systems

Why are organizations shifting to multi-agent designs?

Because no single AI model or agent can effectively handle the complexity of modern business tasks like legal contract analysis, HR policy handling, cross-system automation, and multilingual summarization. Instead, multiple specialized agents — each optimized for reasoning, retrieval, orchestration, validation, and compliance — work together to create richer, more robust outcomes.

But collaboration among agents is non-trivial. Without a well-structured communication model, the system quickly becomes fragile, opaque, and difficult to scale.

That’s where A2A and MCP come into play.

A2A Protocol: Point-to-Point Agent Collaboration

The Agent-to-Agent (A2A) model is a direct, decentralized approach where each AI agent exposes its own interface (usually via API or message bus) and communicates with other agents independently. Agents can call each other, pass data, and await responses — much like microservices in traditional software architecture.

Architectural Design

Each agent in an A2A system:

• Is independently hosted and deployed (often in containers or serverless functions)
• Maintains its own internal memory and local context
• Communicates via API (REST/gRPC), message queues, or brokerless messaging
• Needs to know the endpoint, expected schema, and response structure of other agents

The typical A2A system ends up forming a point-to-point network topology where agents interconnect as needed. Over time, this grows into a “spaghetti” architecture — a tangled mesh of dependencies that increases operational complexity.

Technical Characteristics

• Agents are tightly coupled but autonomous
• Communication is often synchronous but may include async tasks
• Each agent may handle orchestration logic locally
• Fault handling and retries must be implemented at each call point
• There is no centralized memory or shared history — each agent has only local context

Benefits of A2A

• Flexibility: New agents can be introduced easily with their own unique logic
• Autonomy: Each agent can evolve independently, use different LLMs or prompts
• Parallelism: Multiple agents can run tasks concurrently
• Microservice Compatibility: Aligns well with modern enterprise systems

Challenges of A2A

• Scalability Bottlenecks: The system grows at O(n²) complexity for n agents
• Debugging Complexity: Distributed traceability and failure points are harder to manage
• Maintenance Overhead: Changes to one agent can ripple across many dependent agents
• Context Fragmentation: No unified memory or task history means poor coordination

This model often starts strong in small systems but struggles under scale. Many organizations using A2A encounter limitations as they expand agent counts, leading them to reevaluate their architecture.

MCP: The Model Context Protocol

The Model Context Protocol (MCP) was introduced as a response to the chaotic growth of A2A systems. MCP offers a centralized, structured, and protocol-driven approach to agent orchestration. Instead of agents calling each other directly, a central orchestrator (often powered by an LLM) manages the execution, state, memory, and function chaining of all tools and agents.

Architectural Design

In MCP-based systems:

• A central orchestrator receives the user query or system goal
• It maintains a shared context, including:
• System messages
• Tool and agent descriptions
• Memory/history of prior steps
• Intermediate results
• The orchestrator uses structured prompts and reflection to choose the next action
• Tools/agents are invoked as functions, each with a defined schema

Agents are not aware of each other — they are tools waiting to be called, and the orchestrator determines how to compose them into a solution plan.

Technical Characteristics

• Stateless tools (agents) are invoked via semantically described calls
• Memory is maintained at the orchestrator level and shared across calls
• Responses are structured using JSON or function-call delimiters
• Self-reflection, retrying, and validation can be embedded within the orchestrator loop
• Enables easy integration with plugins, APIs, and traditional tools

Benefits of MCP

• Centralized Memory: Every step is recorded and used to inform the next
• Improved Reasoning: The orchestrator can plan, reflect, and adjust dynamically
• Extensibility: New tools are plugged in via schema, not custom code
• Traceability: Execution logs, context, and decision flows are centrally available
• Security: Better for enterprise governance, auditing, and compliance

Challenges of MCP

• Initial Complexity: Requires upfront planning of context, schema, and tool metadata
• Less Flexibility at the Edge: Tools must conform to orchestrator’s structure
• Dependence on LLM Quality: The orchestrator must reason well to avoid bad plans

Overall, MCP introduces predictability and power into agent systems, but at the cost of initial agility.

Comparison: A2A vs MCP

Applications in Real Systems

Where A2A Shines

A2A systems excel in fast-moving, modular environments where different teams control different agents, and rapid iteration is needed. Examples:

• Customer Support Automation: Retrieval agents, summarizers, sentiment analyzers
• Low-latency Pipelines: Inference chains with strict SLAs
• Prototyping AI Workflows: Quick POCs using containerized agents

A2A is often the first architecture teams try when moving to multi-agent design.

Where MCP Wins

MCP truly shines in enterprise-grade, autonomous AI systems that demand:

• Shared memory, such as legal document understanding
• Regulated flows like healthcare or finance
• Multi-turn logic, reflection, or tool optimization
• Auditability and chain-of-thought traceability

Many enterprises building copilots for HR, operations, or compliance prefer MCP for its structure and reliability.

Emerging Trends

As agent ecosystems mature, hybrid models are emerging:

• A2A at the edge for fast, specialized tools
• MCP at the core for reflection, orchestration, and governance

Future systems may include:

• Semantic routing: Auto-selecting the best agent for a task using embeddings
• Dynamic memory graphs: Agents subscribing to memory updates
• Tool marketplaces: Plug-and-play capabilities for LLM agents

In all of these, protocols that offer modularity, observability, and planning will define the next wave of AI system design.

Strategic Considerations for Enterprises

From a design standpoint, the choice between A2A and MCP impacts everything from dev velocity to compliance posture. Enterprises must ask:

• How dynamic is our agent environment?
• Do we require shared memory and orchestration?
• What is the cost of failure and miscoordination?
• Can we tolerate custom integration or need schema-first design?

Increasingly, the MCP-style orchestration is being adopted not just for technical superiority, but because it allows systems to grow intelligently, adapt, and remain governable.

These are not just design decisions. They shape the personality of AI systems — how they behave, interact, and learn over time.

Final Thoughts

The future of AI systems is multi-agent — but more importantly, it’s multi-architectural. A2A protocols offer simplicity and agility, while MCP brings reasoning, structure, and control.

The architectures we choose today are the foundations of tomorrow’s autonomous systems. Those who understand not just how agents work, but how they work together — will define the next generation of intelligent software.

Both A2A and MCP signal a fundamental shift in AI design: from single-turn chatbots to agentic ecosystems that plan, reason, and collaborate. For practitioners, architects, and researchers, understanding these paradigms is the gateway to building scalable, secure, and powerful AI solutions.

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Published via Towards AI