The Smartest Way to Manage Context in AI (and Actually Do Something With It)

Some AI systems can seem forgetful, like you're starting from scratch every time you interact. Other times, they could respond as if they knew your whole history. Why such a massive gap in user experience? The answer lies in how AI manages context, an often-overlooked backbone of intelligent, useful AI.

By the end of this article, you’ll know the smartest way to handle context and how to turn it into something that delivers real impact.

What is context in AI?

From the words you use, to the conversations you have, to the industry you're in, your location, or even the time of day, all of that is context. And AI uses all of it (when available) to generate more accurate, useful, and relevant responses. Context in AI is the background knowledge, memory, and environmental awareness that help AI understand what you mean, not just what you say.

How can you manage context in AI agents?

When context isn't managed well in AI agents, users often have to repeat themselves, which breaks the flow of conversation and makes the AI feel less intelligent or helpful. Misinterpretations, irrelevant answers, or inconsistencies cause users to lose trust in the system, especially when they expect it to remember previous inputs or adapt to their needs. In critical situations, poor context handling can even lead to harmful or misleading recommendations.

To manage context properly, you need a system that can remember user inputs across sessions, retrieve relevant information instantly, and power intelligent responses over time. That system is called MCP.

What is MCP?

MCP is short for Model Context Protocol. It defines how an AI agent stores, retrieves, and manages information across interactions.

Types of MCP.

A Model Context Protocol(MCP) consists of two main approaches:

i. Basic MCP

ii. Unified MCP Gateway

I. Basic MCP is an approach in which the model only remembers what happened during the current conversation. Once the chat ends, the memory resets. With a basic MCP, context is handled only within a single interaction. For example, if you're chatting with a support bot, the bot can remember your question and use that information to respond. But as soon as the session ends, the memory disappears. If you return later, it won’t know who you are unless you tell it again. Basic MCPs are great for Customer support bots.

ii. Unified MCP Gateway is a context system that connects multiple conversations, users, and models through a shared, persistent memory infrastructure. Instead of memory being tied to a single session, the gateway remembers past interactions, connects data across tools and models, and personalises experiences over time. Unified MCP Gateway has taken context management to the next level and is the recommended choice for products that need long-term memory. For example, personalised AI tutors. They need to understand and remember their users, to be effective and seen as indispensable to the users.

What Does It Take to Make Context Useful?

It’s one thing for your AI to remember past interactions. It’s another thing entirely for it to use that memory to take meaningful, real-world action. Context alone doesn’t create value or make users stay. For example, if your AI knows someone wants to send a weekly report but can’t actually send that report or create it in the right place, then it’s still incomplete. The real value of such an AI would only be seen when context is followed by action.

Your product shouldn't just understand context, but use it to maximise its impact on the user. Execution is what transforms a context-aware assistant into a truly intelligent product.

To build trust and feel useful, your AI needs three things:

(1) The AI agent,

(2) the memory (an MCP Gateway),

(3) An execution layer that connects to tools.

1. The AI agent: The AI agent is the core intelligence of your product. It’s what interprets natural language, understands user intent, and makes decisions. For example, Large language models (LLMs) are trained on massive amounts of data and can understand, reason, generate, and even hold a short-term conversation. But on their own, they can’t remember anything long-term, and they can’t take real-world actions like sending emails or updating a doc. The AI agent is like the brain, understands the structure of that request, and forms a plan to solve it.

2. The memory(an MCP gateway): This is what gives your AI a working memory. It stores, retrieves, and manages relevant user data across different sessions, tools, and prompts. Unlike the AI agent, which forgets everything when a session ends, the MCP Gateway ensures your AI knows who the user is, what they’ve done before, and how they like things. For example, let’s say a user previously asked the AI to send a weekly update every Friday at 3 p.m. With an MCP Gateway in place, the AI doesn't need to be told this again. When the user later says, “Send this week’s update,” the gateway reminds the AI of the last format, the recipients, and preferred tools. Without the MCP Gateway, the AI would be starting from scratch every time, and the user would be forced to repeat instructions. The Gateway ensures continuity, personalisation, and trust.

3. The execution layer: The execution layer is the infrastructure that connects your AI to real-world services and tools like Slack, Gmail, Notion, Google Docs, databases, and CRMs, and more. It’s the system that carries out commands your AI generates, securely, reliably, and instantly.

So what does this look like in practice?

Let’s say I tell my AI assistant:

"Create a Google Doc titled ‘Justice Family’ and send it to the Slack channel with a message saying ‘Hello, I’ve created the doc.’”

With FASTNucl, here’s what happens:

• The AI agent interprets the request.

• The MCP Gateway recalls your preferred doc format, past titles, and Slack workspace.

• The Execution Layer (via FASTNucl) creates the doc in Google Drive and posts the message in the right Slack channel, all automatically.

Want to integrate an execution layer into your AI product?

Check out ucl.dev and explore the FASTNucl docs. It’s the fastest way to turn your AI from smart… to actually useful.