What are MCPs in AI? (Multi-Headed Control Policies)

— Issue #16 of The Artificial Newsletter

🤖 What are MCPs in AI? (Multi-Headed Control Policies)

In AI, Multi-Headed Control Policies (MCPs) refer to systems that can manage multiple tasks or objectives simultaneously. It’s like having one brain (the AI) that can think and act on different problems at the same time—just like a conductor leading multiple sections of an orchestra.

📌 How Does MCP Work?

MCPs operate by having different heads or branches for each task:

  • Each head is responsible for a specific part of decision-making.

  • They share some common knowledge but work independently for their specific goals.

  • When it’s time to make a decision, the right head is triggered

🎯 Example: Self-Driving Car

Imagine a self-driving car:

  1. Head 1: Detects and avoids obstacles.

  2. Head 2: Follows road signs and traffic lights.

  3. Head 3: Manages the speed and lane positioning.

All three heads are controlled by a single MCP, which allows the car to drive safely and efficiently at the same time.

🔄 Why Multi-Headed?

The reason for using multiple heads is that each task often requires different strategies. MCPs allow the AI to:

  • Focus on each specific task independently.

  • Optimize decision-making without interference from other tasks.

  • Handle complex, multi-dimensional problems smoothly.

💡 Think of it like this:

When you are:

  • Cooking food → One part of your brain manages the ingredients.

  • Listening to music → Another part processes the lyrics.

  • Talking to a friend → Yet another part handles the conversation.

Each of these is like a separate head in an MCP.

📌 Why Should You Care?

Understanding MCPs can help you grasp how advanced AI agents are designed to perform multitasking efficiently. It’s the key to building smarter, more efficient AI systems that can operate seamlessly in real-world scenarios.

🛠️ DIY Project: Build Your Own MCP-powered AI Agent!

This week, let's get hands-on. We will create a simple AI agent with three heads using a Python-based framework:

  1. Head 1: Email summarization—analyzes and extracts key points from your inbox.

  2. Head 2: Calendar scheduler—automatically sets up meetings based on email context.

  3. Head 3: Task prioritization—categorizes your daily tasks based on urgency.

Next issue, we’ll continue exploring how these concepts apply to AI Agents and how you can start building your own with low-code platforms.

Until then, keep learning and stay curious! 🚀

Cheers,

The Artificial Newsletter