Don’t let your AI fly blind

Over the last few decades Integration has revolutionized systems by giving them the ability to talk to each other and perform tasks as we as humans do. It is no different this time. We went from TCP/IP to HTTP to RCP to APIs to Microservices to GraphQL and now MCP or A2A will revolutionize how systems interact. Will explain why this is important and why as product teams we should be designing and envisioning our products keeping these trends in mind to future proof your products.

Before we dive into MCP and what it is ? Let's first understand how we got here…

History & the fundamentals

The History

• The Foundation (TCP/IP):

It all started here. Think of TCP/IP as the fundamental digital postal service or phone network. It established the basic rules allowing computers to reliably send and receive packets of information across networks, forming the backbone of the internet. Without it, widespread system communication wasn't feasible.

• Application Conversations (HTTP & RPC):

Building on TCP/IP, we needed ways for applications to talk. HTTP (Hypertext Transfer Protocol) became the standard for web browsers and servers to request and share web pages and data – like using a standard form for requests. Around the same time, RPC (Remote Procedure Call) emerged, aiming to make calling functions on a remote computer seem almost as simple as calling one locally – like giving direct instructions over a dedicated line.

• Opening the Doors (APIs - especially REST):

The real explosion in integration came with APIs (Application Programming Interfaces), particularly Web APIs using HTTP, often designed using REST (Representational State Transfer) principles. This was like businesses publishing a clear service menu (the API) that anyone could use over the web. It allowed different systems, even from different companies, to interact easily, powering the rise of web services, mobile apps, and Software-as-a-Service (SaaS).

• Breaking it Down (Microservices):

As applications grew complex, the Microservices architecture gained popularity. Instead of one giant program, applications were built as a collection of small, independent services. These services communicated heavily with each other, typically using the APIs developed in the previous era. This increased flexibility and scalability but also significantly increased the amount and complexity of internal system communication.

• Smarter Data Requests (GraphQL):

With complex applications and mobile apps needing specific data efficiently, GraphQL offered an evolution. Instead of APIs defining fixed data responses (like REST often does), GraphQL allows the client system to specify exactly what data fields it needs in a single request. This prevents getting too much unnecessary data or having to make multiple calls to get everything needed.

The Need for More Context (Leading to MCP):

Each step enabled more powerful interactions but also surfaced new challenges. Managing the intricate web of communication in microservices, ensuring consistency, and understanding the context of interactions became increasingly difficult. This growing complexity sets the stage for the next evolution...

What is a Model Context Protocol (MCP)?

Previous communication protocols (like HTTP, APIs, GraphQL) focus mainly on sending data or commands. Model Context Protocol (MCP) goes a step further by standardizing how systems exchange context along with the data or command. AI agents need more context to understand under what circumstance it needs to call a particular service.

This context includes information like:

• The overall goal of the interaction.
• What happened immediately before this request.
• The current state relevant to the request.
• The specific reason why the request is being made.
• Any rules or preferences for handling the request.

Essentially, MCP ensures that when systems talk, they don't just send isolated pieces of information, but also the surrounding circumstances and intent needed for true understanding.

Why is this Important, Especially for AI?

Systems, particularly AI models and autonomous agents, need context to function effectively. Without it, they operate with limited understanding, leading to errors or suboptimal actions.

MCP provides a structured way to deliver this necessary context, enabling systems to:

• Make Better Decisions: Understanding the 'why' and 'what happened before' leads to more accurate responses.
• Collaborate on Complex Tasks: Multiple systems or agents can work together more effectively if they share a common understanding of the goal and current situation.
• Increase Efficiency: Reduces the need for clarification requests and misunderstandings.

For AI, having this context readily available is crucial for moving beyond simple command execution towards more intelligent, adaptive behavior.

Designing systems with the ability to share and consume context prepares them for more advanced integrations and AI-driven capabilities.

MCP's Role in the Journey to AGI

Moving from today's narrow AI (ANI) to Artificial General Intelligence (AGI) requires AI to understand complex context and collaborate effectively. MCP provides a standard way to communicate this vital context—goals, history, intent.

This enables AI systems to:

• Collaborate more effectively on diverse tasks.
• Reason more deeply, beyond simple instructions.
• Adapt dynamically to changing situations.

Essentially, MCP offers a necessary communication foundation for the context-rich interactions needed for AGI's broader, adaptable intelligence.

Conclusion

For product teams, the message is clear: the future of integration involves more than just connecting pipes; it involves enabling meaningful conversation. Thinking about how your systems can produce and consume context – understanding the 'why' behind interactions – is no longer just a forward-thinking concept. It's becoming a foundational requirement for building resilient, intelligent, and future-proof products ready for the age of AI. Designing with context in mind today paves the way for seamless integration with the intelligent systems of tomorrow.