Imagine you're catching up with a friend. You don't start every conversation from scratch, retelling your entire life story. Your friend remembers your job, your hobbies, and that you were planning a trip last month. All this shared information—the history, the environment, the implicit understanding—is context. It's what makes the conversation flow naturally.

Artificial intelligence systems need context for the same reason. In AI, context is all the background information an AI model uses to understand a situation and respond appropriately. It's more than just the last thing you said. It can include:

• Conversation History: What has been said so far?
• User Information: Who is the user? What are their preferences?
• Documents & Data: What files or database records are relevant?
• Environment: What application is the AI running in? What tools can it use?

When an AI has the right context, its performance improves dramatically. Think of a coding assistant. If it has the context of your entire project's codebase, it can offer relevant code completions and catch bugs specific to your work. Without that context, it can only give generic advice.

Proper context management makes AI interactions feel smarter, more personal, and less frustrating. You don't have to repeat yourself constantly. The AI anticipates your needs because it understands the broader picture. It can pull information from different sources—like an email, a calendar, and a contact list—to perform a complex task, such as scheduling a meeting.

Humans build context naturally over a lifetime. AI models aren't so lucky. They have a fundamental limitation: a finite context window. This is like a form of short-term memory. The model can only pay attention to a certain amount of information at one time.

If a conversation or a document gets too long, the earliest parts get pushed out of the context window. The AI literally forgets what happened at the beginning. This is why you might find yourself re-explaining details to a chatbot midway through a long conversation.

This problem gets even harder when an AI needs to interact with different tools, databases, or APIs. Each tool speaks its own language. Connecting the AI to each one requires custom engineering work, creating brittle, complex systems that struggle to share context between different tasks. The AI might know about your project files, but can it access your team's issue tracker? Getting them to talk is a huge challenge.

To overcome these challenges, AI systems need a standardized way to handle information. Instead of building dozens of one-off, custom connections for every data source and tool, developers need a universal adapter—a common protocol that lets any tool or database provide context to an AI in a format it can understand.

This would create a more organized and scalable way to build AI applications. A standardized protocol would allow an AI to dynamically connect to new tools, manage its memory efficiently, and maintain context across complex, multi-step tasks.

Without a standard, building powerful AI applications is like building a tower of mismatched blocks—it's unstable and hard to expand. With a standard, we have a solid foundation to build upon.