As the complexity of agentic AI systems grows, so does the need for a structured approach to designing and deploying them. Enter MCP: a practical architectural framework for building intelligent agents. Short for Model-Compute-Prompt, MCP helps developers break down agentic systems into three functional layers—each with distinct responsibilities, tooling requirements, and optimization strategies. This blog delves into Agentic AI with the MCP stack in detail, demonstrating how it supports agent autonomy and reliability, and provides practical guidance for implementation in both cloud and edge environments.
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What is MCP?
MCP is an emerging design pattern for building agentic AI systems. It separates responsibilities into three core layers:
- Model – the reasoning engine (usually an LLM)
- Compute – the orchestration and execution infrastructure
- Prompt – the structured interface between user goals and agent behavior
Each layer can evolve independently and allows for modular, scalable agent architectures.
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Layer 1: The Model Layer
At the heart of every agent is a reasoning model, typically a large language model (LLM). This layer is responsible for:
- Understanding user input
- Planning the next actions
- Interpreting tool outputs
- Generating final responses or summaries
Common Tools:
- Azure OpenAI (GPT-4, GPT-4o)
- Anthropic Claude
- Open-source models via Ollama, Hugging Face, or local deployment
- Fine-tuned models for domain-specific reasoning
Best Practices:
- Choose models based on use case (e.g., Claude for long context, GPT-4o for speed + performance)
- Use prompt templates to guide consistency and safety
- Apply rate limits and token controls at the model access level
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Layer 2: The Compute Layer
This layer is where actions happen. It’s responsible for:
- Executing API calls and tools
- Handling retries and error correction
- Logging decisions
- Orchestrating multi-agent workflows
It includes the agent runtime, external tools, memory systems, and infrastructure.
Common Tools:
- LangChain and LangGraph for orchestration
- Python and FastAPI for execution logic
- AWS Lambda, Azure Functions for serverless compute
- Docker, Kubernetes, Edge devices for deployment
Best Practices:
- Separate reasoning from execution: the model plans, the compute acts
- Use secure tool wrappers (e.g., for databases, webhooks, email)
- Add observability via structured logs, metrics, and traces
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Layer 3: The Prompt Layer
This is the most human-centric layer. Prompts are the interface between users and models. They define the agent’s instructions, structure, and tone. In many ways, prompts are the “brainstem” of an agentic system.
Components of Good Prompt Design:
- System prompts: Define the agent’s role and behavior
- User prompts: Natural language input from users
- Tool usage prompts: Instruct the model when and how to use specific tools
- Output format prompts: Ensure consistent and parseable results (e.g., JSON, Markdown)
Best Practices:
- Use prompt templating libraries (e.g., LangChain’s PromptTemplate)
- Include examples (few-shot prompting) to improve model performance
- Add response constraints to control agent output
- Version your prompts like code, and test regularly
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Benefits of the MCP Framework
| Benefit | How MCP Delivers It |
| Modularity | Each layer can be upgraded or replaced independently |
| Debuggability | Easier to pinpoint errors in reasoning vs. execution |
| Scalability | Compute layer handles scale, model layer focuses on logic |
| Reusability | Prompts and tools can be reused across agents and tasks |
| Vendor Agnosticism | Swap models, runtimes, or APIs with minimal refactoring |
Real-World Example: Agentic Travel Assistant
Imagine building a travel agent bot that can:
- Receive a user prompt: “Book me a hotel in Boston near MIT from June 20–22.”
- Plan: Understand the request and extract parameters
- Act: Use a hotel API via a tool wrapper
- Confirm: Generate a response with booking info and summary
Using MCP:
- Model: GPT-4 interprets the prompt, generates tool usage plan
- Compute: Python function calls hotel API, handles response
- Prompt: Defines format for requests, tool invocation, and user reply
Final Thoughts
MCP is not a product or a framework—it’s a mental model and design philosophy. By cleanly separating reasoning (Model), execution (Compute), and instruction (Prompt), it allows developers to build flexible, testable, and production-grade agentic systems.
As teams scale agentic AI across applications—from marketing to ops to R&D—adopting MCP will help ensure architectural clarity and operational resilience.
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FAQs
What does MCP stand for in agentic AI development?
MCP stands for Model-Compute-Prompt, a layered architectural approach for building agentic AI systems with clear separation between reasoning, execution, and instruction.
Why use the MCP framework when building agents?
MCP promotes modularity, debuggability, and scalability by isolating each functional layer, making systems easier to test, maintain, and evolve independently.
What is the Model layer responsible for?
The Model layer handles all LLM-related reasoning—interpreting inputs, generating plans, choosing tools, and crafting responses using models like GPT-4 or Claude.
What does the Compute layer include?
The Compute layer executes the actual actions—API calls, database queries, notifications—and handles error management, retries, and observability.
What is the role of the Prompt layer?
The Prompt layer defines how the model receives instructions, formats responses, and interacts with users and tools. It includes system, tool, and output prompts.
Can I change the model in MCP without affecting other layers?
Yes. MCP’s modular design allows you to swap out models (e.g., GPT-4 → Claude or open-source) without needing to change compute logic or prompt structures.
Is MCP tied to any specific library or platform?
No. MCP is a design philosophy, not a software package. You can implement it using LangChain, LangGraph, FastAPI, AWS, Azure, or any tech stack of your choice.
How does MCP improve debugging in agentic systems?
Since each layer is isolated, developers can test prompt logic separately from tool execution, making it easier to find and fix failures.
Is MCP suitable for real-time or edge-based agent systems?
Yes. MCP works in cloud and edge environments, allowing models to run locally (via Ollama) while compute tools and prompts stay the same.
What are some tools commonly used in the MCP stack?
- Model: OpenAI, Anthropic, Hugging Face
- Compute: Python, LangChain, LangGraph, serverless functions
- Prompt: PromptTemplate libraries, YAML/JSON-based instruction sets
