How To Build A Master AI to Control AI Agents
MCP, which stands for Model Context Protocol, is an open standard designed to standardize how AI models connect and interact with external data sources, tools, and systems. Think of it as a universal translator or a "USB-C port for AI" enabling seamless communication between AI applications and the diverse environments they need to operate within.
MCP is a significant step towards creating a more connected and capable AI landscape by providing a standardized way for AI models to understand and interact with the world's vast and varied information and functionalities.
I want to take the next step.
Could we use MCP to coordinate AI agents as they upgrade the MCP itself and re-write their own code? I am building this to find out.
More below. First, we need to set the table.
MCP aims to create a common language and framework for AI models to access and utilize context from various sources. This eliminates the need for developers to build custom integrations for every new tool or data source, which can be complex and time-consuming (often referred to by fellow nerds as the "N×M integration problem").
The N by M integration problem describes the difficulty and rapidly increasing complexity that arises when you need to connect multiple systems, components, or services from one group (N items) with multiple systems, components, or services from another group (M items).
There will be NO math on the test. I promise. This isn’t very technical.
Here's a breakdown:
N: Represents the number of items in the first set (N different applications, N data sources, N types of AI models).
M: Represents the number of items in the second set (M different platforms, M external tools, M user interface frameworks).
The Problem:
If each of the N items needs to connect directly and uniquely with each of the M items, the number of required individual integrations or adapters is N multiplied by M (N × M).
If you have 3 applications (N=3) that each need to integrate with 4 different databases (M=4), you might need to build and maintain 3 × 4 = 12 unique integrations.
If N grows to 10 and M grows to 10, you suddenly need 10 × 10 = 100 integrations.
If N is 50 and M is 50, you're looking at 2,500 integrations.
Why it's a Problem:
Complexity: As N or M (or both) increase, the number of integrations grows multiplicatively, leading to an explosion in development effort, cost, and time.
Maintenance Nightmare: Each integration point needs to be maintained, updated, and monitored. Changes in one system can have cascading effects, requiring updates to multiple integrations.
Brittleness: The system becomes fragile. If one integration breaks, it can affect multiple functionalities.
Reduced Agility: Adding a new item to either set (a new N+1 application or a new M+1 tool) requires building many new integrations (M new integrations if you add one N, or N new integrations if you add one M). This slows down innovation and adaptation.
Inconsistency: With so many bespoke integrations, ensuring consistency in data handling, security, and functionality across all connections becomes very difficult.
This problem is solved by providing a standardized way to access real-time information. MCP allows AI models to generate more relevant, accurate, and up-to-date responses using this ability to reach out and grab new information. These systems are no longer solely reliant on their training data.
In the context of AI models and tools N might be the number of different AI models/applications, and M might be the number of different external tools, data sources, or APIs. Without a standard like MCP, integrating each model with each tool would quickly become an N×M nightmare. MCP aims to be the "standardized protocol" or "hub" that reduces this to N+M connections to the protocol standard.
MCP promotes a more modular and flexible AI ecosystem. It allows different AI models and applications to connect with a wide array of tools and data services that adhere to the protocol. It simplifies the process of building AI agents and complex workflows by providing pre-built integrations and the flexibility to switch between different LLM providers.
Just as a computer's operating system provides a standard way for different software programs to access hardware (like printers, storage, or network connections) without needing to know the specific low-level details of each component, MCP provides a standard way for AI models to access and use diverse external "tools" (like search engines, APIs, databases) and "data sources" (like files or real-time information).
MCP came about to solve the problems of coordinating these agents: standardization, abstraction of complexity, enabling access to diverse resources, and simplifying development for AI applications that need to interact with the world. It's less about active "control" of multiple agents and more about enabling individual models/agents to function effectively with their environment through a common interface.
The "protocol" nature, standardized request-response patterns, enabling a vast ecosystem of interactions, and facilitating seamless communication between different entities also reminds me of the engines that drive the internet.
HTTP is the fundamental protocol that defines how web browsers request information and how web servers respond, enabling the entire World Wide Web to function through a standardized communication method. In the same way, MCP aims to be a universal protocol defining how AI models make requests to external tools or data sources and how those services respond.
Key Features and Concepts
Client-Server Architecture: MCP typically operates on a client-server model.
MCP Hosts: These are the AI applications (e.g., chatbots, IDE assistants) that initiate connections.
MCP Clients: These are components within the host application that manage communication with specific MCP servers.
MCP Servers: These are lightweight programs that expose specific capabilities (tools, data) from external systems according to the MCP specification.
Defined Primitives: MCP organizes interactions through standardized primitives:
Tools: Executable functions that LLMs can call to perform actions (send an email, query a database, engage with an API).
Resources: Data sources that LLMs can access to retrieve information (files, database records, API responses).
Prompts: Reusable templates and workflows to guide the interaction between the LLM and the server.
Open Standard and Open Source: MCP is an open protocol with publicly available specifications and software development kits in various programming languages, but obviously I’ll be using Python.
If you are clever, you can intelligently chain these together and form very powerful systems.
Where Did MCP Come From?
MCP was initially introduced by Anthropic.
Now MCP is exploding across the scene.
It has since seen adoption and support from major players in the field, including OpenAI, Google DeepMind, Microsoft (e.g., in Copilot Studio and Azure OpenAI), and AWS. A growing ecosystem of toolmakers and developers are building MCP servers and integrating the protocol into their platforms.
What’s Next In Model Context Protocols?
The continued evolution of Model Context Protocols is poised to significantly shape how AI models and agents interact with the digital world.
We can anticipate future advancements focusing heavily on establishing more robust security and trust frameworks, moving beyond current authentication to incorporate granular, dynamic permissions, verifiable credentials for tools, and even privacy-preserving techniques for handling sensitive context.
This will make for a much more secure operating environment and that will drive more development and adoption, the start of a reinforcing loop.
Simultaneously, MCPs will likely expand to support richer context primitives and interaction models, embracing multi-modal data like images and audio as first-class citizens, better managing stateful and long-running operations, and enabling real-time streaming context for dynamic environments. Alongside these enhancements, expect more sophisticated mechanisms for dynamic tool discovery, the introduction of workflow and orchestration primitives directly within the protocol to manage complex multi-tool tasks, and improved version management for both the protocol and the tools themselves.
Giving these AI Agents better ability to summon the correct tool, and then enhancing their coordination with other agents will suddenly give developers a surge in their design and engineering capabilities. Think of the knock-on impact of that.This will be drive an improved developer experience, featuring standardized debugging, tracing, and validation frameworks, alongside more mature SDKs.
MCPs will evolve to support more bidirectional and proactive interactions, allowing external systems to push relevant context to agents and enabling collaborative context-building among multiple AIs.
Building the Ultimate AI System to Command and Control AI Systems
Okay, here’s how I’m building out this system where my AI agents can coordinate their own upgrades, including evolving the MCP server we use.
First, I established our foundational MCP infrastructure.
This means deploying the initial MCP servers that provide access to essential services. A critical early tool I focused on getting running first was a version-controlled code repository service. This repo hosts the agents' source code and the definitions for MCP itself. My agents have MCP-driven capabilities to read from this repository and, importantly, propose changes like pull requests. Complementing this, I (borrowed then upgraded) an MCP-accessible build and deployment service. This service is responsible for taking code from the repository, handling any compilation or packaging, and deploying it to the relevant agent instances.
As of this writing, this is “where I’m at” in the build.
Next, I'll be integrating a suite of advanced development and analysis tools, ensuring each is available as an MCP service. This suite will include services for static and dynamic code analysis, which my agents will use to understand existing codebases. I’m also hooking up powerful code generation and modification models, likely specialized LLMs fine-tuned for coding, as MCP tools. My agents will learn to query these tools with specific objectives, such as "refactor this module to be compliant with MCP version 2.1" or "develop a new feature based on these updated specifications." An automated testing framework, also callable via MCP, is absolutely essential. I'm programming my agents to rigorously test any proposed code changes or MCP upgrades within a sandboxed environment before any broader deployment.
To manage the evolution of MCP itself, I'm creating a dedicated "MCP Governance Service." This service, naturally accessible via MCP, will be the authoritative source for announcing new versions of the protocol, detailing any breaking changes, providing migration guides, and serving the new protocol definitions or libraries. My agents will be programmed to periodically query this service. When a new MCP version is detected, an agent, or perhaps a designated group of agents, will initiate an upgrade proposal. This process will involve leveraging the code modification tools I mentioned earlier to adapt their own code, and potentially the code of other agents or the MCP servers they rely on, to the new MCP standard.
Coordination among my multiple agents during these complex upgrades and rewrites is paramount. I’m setting up "Coordination Services" accessible via MCP, and also building peer-to-peer coordination logic directly into the agents, using MCP as the underlying transport layer. These services and logic will enable agents to achieve consensus on upgrade plans, timing, and the division of responsibilities. This will likely involve implementing a distributed ledger or a shared state mechanism where agents can log their upgrade status, dependencies, and test results. I'm designing this system to manage phased rollouts; for instance, one agent or a small cohort will act as a canary in the agentic coal mine, fully upgrading and undergoing thorough testing before the rest of the collective follows suit. And, of course, robust rollback procedures, triggered via MCP commands, will be in place to revert to a previous stable state if any upgrade introduces instability or fails. More on that below. First, I need to finish freaking you out about the self-upgrading nature of this system.
The core logic of my AI agents is architected specifically for this self-improvement cycle. They are proactive, continuously monitoring their own performance, actively checking for updates to MCP or their operational code, and identifying opportunities for optimization. This means they autonomously initiate the process: proposing changes, invoking the necessary MCP tools for development and testing, participating in the established coordination protocols, and ultimately deploying the verified updates to themselves and potentially to the MCP services they depend upon.
That’s right, they are going to upgrade themselves.
Some of you are screaming THIS IS HOW TERMINATOR STARTS.
Not so fast.
This MCP infrastructure includes a version-controlled code repository service. This won't just store the latest code; it will be the heart of our "time capsule" strategy. At key junctures (after a successful major upgrade is validated, before my agents attempt a particularly ambitious evolutionary leap) the system creates a comprehensive "time capsule." This capsule includes be a coordinated set of tags across all relevant repositories: the agents' source code, the specific version of MCP in use, the configurations of all critical MCP tools and servers, and even snapshots of essential persistent data that defines the agents' operational state. Each tag represent a known-good, fully functional state of the entire ecosystem. These agents will use MCP to interact with the version control service to list these "time capsule" tags and understand their contents and a "manifest" of what state they represent.
This ties directly into the build and deployment service and our rollback capabilities. If my automated monitoring systems detect that a recent evolution has resulted in a "negative mutation" (e.g., significantly degraded performance, critical errors, undesirable emergent behaviors) it will trigger a reversion process. My agents, using MCP are able to initiate a rollback to a specific "time capsule" tag. The coordination services manage this process across the distributed agent collective to ensure a synchronized revert. The build and deployment service, callable via MCP, fetches the exact versions of all components (agent code, MCP definitions, tool configurations) specified in that time capsule tag from the version control system and redeploys them. This ensures we can cleanly and reliably restore the entire system to a previously stable and desirable state.
The automated testing framework is also crucial here. Before a state is officially designated as a "time capsule" it will have undergone rigorous testing. This gives us high confidence in the integrity of these rollback points.
As you can imagine, the "MCP Governance Service" and the "Coordination Services" play a role in managing information about these time capsules, ensuring that all agents are aware of available stable states they can revert to. This approach gives us the confidence to allow for more experimental evolution, knowing we have a well-defined and tested mechanism to step back if an evolutionary path proves detrimental. This isn't just about reverting code; it's about restoring the entire operational environment to a known-good configuration, a true system-level undo.
If I succeed in building out this system of self-evolving MCP-coordinated AI agents, the "prize" isn't a single trophy, but a cascade of transformative outcomes.
First and foremost, the intellectual and scientific prize would be immense. I’d be creating one of the first truly autonomous, self-improving digital ecosystems. The insights gained into emergent behavior, distributed intelligence, digital evolution, and the principles of self-organization would be unparalleled. It would be a living laboratory for understanding complex adaptive systems, potentially unlocking new theories in AI and beyond. Just witnessing it successfully navigate unforeseen challenges and optimize itself in novel ways would be a profound achievement.
Then there's the technological prize: this system could be pioneering a new paradigm in software engineering. A system that can autonomously maintain, upgrade, debug, and enhance itself, including its core communication protocols, would represent a monumental leap in resilience, adaptability, and efficiency. This would set a new standard for what's possible with AI infrastructure, moving far beyond current automation into genuine operational autonomy. The core technologies and architectural patterns I develop would likely become foundational for future advanced AI systems.
The problem-solving prize is perhaps the most impactful. Imagine deploying such an adaptive AI collective to tackle some of the world's most complex and dynamic challenges: things like energy modeling, intricate scientific research, managing vast global logistics networks, or even exploring and developing entirely new digital services. A system that can learn, adapt, and rewrite its own operational logic in response to the problem space could discover solutions and efficiencies that are currently unimaginable. It will operate at a scale and speed of iteration that human-led teams simply cannot match.
And it will get faster every second.
Whether it's through commercial applications, where such a system could create entirely new markets or provide an unprecedented competitive advantage, or through its application to public good initiatives, the value generated could be extraordinary. The ability to rapidly develop, deploy, and continuously improve sophisticated experiences without constant, granular human intervention for every code change or upgrade would drastically reduce operational friction and unlock new levels of productivity and innovation.
Successfully building this wouldn't just be about the system itself, but about demonstrating a new path forward for AI development: one where AI itself becomes a partner in its own evolution and application, leading to capabilities that amplify our own significantly. That potential to reshape how we approach complex problem-solving and technological development is, in many ways, the ultimate prize that humanity is after.
What a time to be a builder. Wish me luck!
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