Multi-Agent Design Pattern with MCP (Model Context Protocol)
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In today’s AI-driven systems, where intelligent agents perform complex reasoning, planning, and tool usage, there is a need for structured collaboration and contextual awareness. This is where the Multi-Agent Design Pattern (MADP) and Model Context Protocol (MCP) come into play enabling scalable, modular, and context-aware multi-agent ecosystems.
What is the Multi-Agent Design Pattern?
The Multi-Agent Design Pattern is a system architecture approach in which multiple autonomous agents interact with each other to solve tasks. Each agent is assigned a specific role and may:
- Collaborate or operate independently
- Delegate subtasks
- Use specialized tools or services
- Maintain its own memory and context
This pattern is commonly used in:
- AI assistants and copilots
- Autonomous decision-making systems
- Workflow orchestration
- Simulation environments
What is MCP (Model Context Protocol)?
MCP, or Model Context Protocol, is an abstraction introduced in frameworks like Microsoft’s AutoGen, designed to manage context, memory, communication, and identity among large language model (LLM)-based agents.
MCP defines how models (agents) share or isolate context, interact through standardized messages, and maintain memory across turns—without requiring the developer to hard-code the interaction logic for each scenario.
Key Features of MCP
| Feature | Description |
| Context Management | Ensures each agent receives only the relevant conversation history |
| Modular Role Definitions | Agents operate within clearly defined boundaries |
| Message Routing | Communication is standardized through message-passing |
| State Isolation or Sharing | Developers can configure shared or private memory for agents |
| Interoperability | MCP supports different LLMs (e.g., OpenAI, Azure, local models) within the same ecosystem |
MCP in Multi-Agent Architecture
Example Agents:
- PlannerAgent: Interprets high-level tasks and breaks them into subtasks
- ResearchAgent: Searches knowledge bases or the internet
- CoderAgent: Generates code or queries
- ReviewerAgent: Validates outputs and identifies issues
What MCP Does:
- Maintains individual and shared context between agents
- Routes interactions through a defined group or chat interface
- Stores conversation history and tool usage outcomes
- Controls how much memory each agent has access to
How MCP Works (Simplified Flow)
- Agent Definition: Define each agent with a name, role/system message, LLM, and memory type.
- GroupChat Initialization: Agents are added to a GroupChat or multi-agent interaction hub.
- Message Passing: MCP handles who speaks when, what memory they can access, and how messages are delivered.
- Context Tracking: The system ensures only relevant context is exposed to each agent at each turn.
Example using AutoGen in Python:
from autogen import GroupChat, ConversableAgent, UserProxyAgent
agent_a = ConversableAgent(name=”Coder”, system_message=”Write Python code.”)
agent_b = ConversableAgent(name=”Reviewer”, system_message=”Review the code.”)
user_proxy = UserProxyAgent(name=”User”)
groupchat = GroupChat(agents=[user_proxy, agent_a, agent_b], messages=[], max_round=3)
MCP ensures that agent_b (Reviewer) only sees the output from agent_a (Coder) and not unrelated context unless configured otherwise.
Benefits of Using MCP in Multi-Agent Systems
| Benefit | Description |
| Modularity | Clean separation of roles and logic for each agent |
| Context Safety | Avoids overwhelming agents with irrelevant conversation history |
| Tool Integration | Agents can have custom tools and APIs |
| Security | Context exposure can be restricted by agent |
| Scalability | New agents can be added with minimal refactoring |
Real-World Use Case: AI-Powered Data Assistant
Objective:
Build a multi-agent assistant that takes a user’s query, writes code, executes it, and explains the result.
Agents Involved:
- PlannerAgent – Analyses intent and defines steps
- CodeWriterAgent – Writes pandas or SQL code
- ExecutorAgent – Runs the code securely
- SummarizerAgent – Summarizes the outcome
Using MCP:
- Each agent works with a precise context
- CodeWriter receives only the necessary parts of the plan
- Executor only sees the final code to run
- Summarizer gets the result plus original query
This approach ensures security, modularity, and explainability.
Challenges and Considerations
| Challenge | Notes |
| Coordination Overhead | Requires careful design to avoid unnecessary context propagation |
| Latency | Chained agent execution can add response time |
| Debugging Complexity | With multiple agents, isolating bugs becomes more complex |
| Resource Management | Each agent may invoke LLMs, impacting cost and performance |
Popular Frameworks Supporting Multi-Agent + MCP
- AutoGen (Microsoft) – Native support for MCP and multi-agent orchestration
- LangGraph (by LangChain) – State-machine-based agent orchestration
- CrewAI – Python-based orchestration with task delegation
- OpenAgents / Autogen Studio – Experimental and visual platforms for agent development
Conclusion
The Multi-Agent Design Pattern, combined with the Model Context Protocol (MCP), provides a solid foundation for building scalable, modular, and intelligent AI systems.
MCP ensures:
- Controlled and relevant context sharing
- Role-based task execution
- Clear communication and memory management among agents
As agent-based AI systems continue to expand in enterprise, research, and consumer applications, leveraging MCP will be key to building robust, efficient, and interpretable solutions.
Geetha S
