The AI Asset Stack: How Skills, MCP Servers, Agents, Prompts & Workflows Fit Together

A founder in Dubai asked us a simple question recently: "Everyone keeps telling me to install skills, connect an MCP server, deploy an agent, save my prompts, and automate my workflows. Are those five different things, or five names for the same thing?" It is one of the most common questions we get, and it is a fair one. The vocabulary around AI building blocks has multiplied faster than anyone's ability to keep up — and almost none of it has been explained clearly in Arabic.

This guide fixes that. We will define each of the five terms in plain language, and then give you a single mental model — we call it the AI Asset Stack — that shows exactly how they fit together and when to reach for each one.

Start with the umbrella term. We use AI assets to mean the installable, reusable building blocks that extend what an AI model can do: prompts, skills, MCP servers, agents, and workflows. They are "assets" in the literal sense — things you acquire, store, reuse, and increasingly trade. A raw chatbot answers questions. AI assets are what turn that chatbot into a system that gets work done. Everything else in this article hangs off that one definition.

Why the five words feel like soup

The confusion is not your fault. The five terms are young, they overlap in real ways, and they are marketed loosely. A skill can contain prompts. A workflow can call an agent. An agent can load a skill and reach a tool through an MCP server. Because the pieces genuinely nest inside one another, vendors collapse them into whatever word is trending that quarter, and the result is a kind of vocabulary soup where "agent" might mean a chatbot, a script, or a fully autonomous system depending on who is talking.

Watch the word "agent" for a single afternoon and you will see it stretched three ways. One vendor calls a scripted chatbot an agent. Another calls a fixed automation an agent. A third reserves it for a system that genuinely plans and decides on its own. All three are selling something real — but they are selling different layers under the same label, and a buyer who cannot tell them apart overpays for autonomy they do not need or underbuilds for a job that required it. The cure is not memorizing definitions. It is having a structure that tells you which layer each word belongs to.

For builders in the Arabic-speaking world, the problem is sharper. Arabic is one of the most widely spoken languages on earth, with more than 168 million internet users. Yet Arabic content remains one of the most underrepresented languages online — by some estimates a low single-digit share of the web, while English alone accounts for more than half. The gap is widest exactly where builders need it most: technical and educational material. So when a team in Riyadh or Cairo tries to learn what these tools are, they are usually reading machine-translated fragments or guessing from English documentation. A clear map matters more here than almost anywhere.

What each of the five actually is

Before the model, four of these five are best understood by the question each one answers.

Prompts — the instruction

A prompt is the text you give a model to steer what it produces. That sounds basic, and a one-off prompt is. But a saved, refined, reusable prompt — a template your whole team uses for product descriptions, legal summaries, or customer replies — is an asset. It captures intent in a repeatable form. Prompts are the cheapest, fastest building block to create and the foundation everything else is built on.

Skills — the know-how

Anthropic defines a skill as a folder containing a SKILL.md file plus any supporting resources — a self-contained unit of domain expertise that an agent discovers and loads only when a task calls for it. The clearest analogy is Anthropic's own: building a skill is like writing an onboarding guide for a new hire. The agent stays a generalist; the skill explains how to operate well in one specific domain — what to do, what to avoid, which steps to follow. Crucially, the Agent Skills format was released as an open standard, so skills are designed to be shared and reused across tools, not locked to one.

Two properties make skills more than just saved instructions. First, they load on demand: an agent can keep dozens of skills on hand and pull the full instructions into memory only when a task matches, which keeps it fast and focused rather than bloated. Second, because a skill can carry scripts and reach into a working environment, it is exactly the kind of asset that needs to be trusted before it is installed — Anthropic's own guidance is to install only from trusted sources and audit what a skill contains. That single line is the entire reason a curated, vetted vault exists: a skill is borrowed expertise, and borrowed expertise is only as safe as its source.

MCP servers — the connection

The Model Context Protocol is an open standard introduced by Anthropic in November 2024 that gives AI models a single, standardized way to connect to external tools, data, and services. Google Cloud describes it as a secure, standardized "language" that lets a model move beyond static knowledge and retrieve live information or take action. The simplest picture: MCP is a universal port for AI — one connector that replaces dozens of brittle, custom-built integrations. An MCP server is the thing on the other end of that port: it exposes a specific tool or data source — your CRM, your database, a search engine — for the model to use. The standard has spread fast: in December 2025 Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation, and it has been adopted by rivals including OpenAI, Microsoft, and Google DeepMind.

The problem MCP solves is worth naming, because it is the reason the standard caught on. Before it, every tool you wanted an AI to use needed its own bespoke integration — connect five models to ten tools the old way and you are maintaining fifty custom connectors, each one fragile. A shared protocol collapses that: build once against the standard and any compliant model can use it. Under the hood, an MCP server can expose a few different things — tools it can call to take an action, resources it can read for context, and reusable prompt templates — but for picking your stack, the one idea that matters is simpler: a skill teaches the model how; an MCP server gives it something real to reach.

Agents — the autonomy

In its widely cited guide Building Effective Agents, Anthropic draws a clean architectural line. Agents are systems where the model dynamically directs its own process and tool use, deciding for itself how to accomplish a task. Give an agent a goal and a set of tools, and it figures out the steps — calling a tool, reading the result, adjusting, calling another — until it is done. That autonomy is powerful for open-ended problems where you cannot predict the steps in advance, but it costs more and is less predictable.

It also carries real risk. An agent that runs for many turns can compound small errors into large ones, and its freedom to choose tools is exactly what makes it harder to control. Anthropic's recommendation is blunt: agents earn their place only when flexibility and model-driven decisions are genuinely needed, and they belong in tested, sandboxed environments with proper guardrails. The autonomy is the feature and the liability in the same breath.

Workflows — the orchestration

From the same source comes the contrast. Workflows are systems where models and tools are orchestrated through predefined code paths. The steps are fixed in advance. A workflow is a recipe: do this, then this, then this, every time. It trades the flexibility of an agent for predictability and consistency — which is exactly what you want for well-defined, repeatable tasks.

Most useful workflows are built from a handful of simple, named patterns rather than anything exotic: chaining, where each step feeds the next; routing, where an early step classifies the input and sends it down the right path; and parallelization, where independent steps run at once and their results are combined. None of these requires the model to make open-ended decisions — the intelligence lives in the design of the path, not in the model improvising at runtime. That is the whole appeal: a workflow you built carefully on Monday behaves the same way on Friday.

The AI Asset Stack

Here is the reframe. Stop thinking of these as five competing products fighting for the same slot. Think of them as five layers, each answering a different question:

Prompts — the intent layer. What do I want done?

Skills — the know-how layer. How is it done well?

MCP servers — the connection layer. What can it reach?

Agents — the autonomy layer. Who decides the steps?

Workflows — the orchestration layer. In what order, and how reliably?

This is the AI Asset Stack. Its value is that it makes the overlaps stop being confusing and start being useful. The layers are not rivals — they compose. A workflow (layer 5) can run an agent (layer 4). That agent can load a skill (layer 2). The skill can contain prompts (layer 1). And at any point, any of them can reach into the real world through an MCP server (layer 3). When someone uses one of these five words loosely, you can now place it: ask which question it is answering, and you know which layer they actually mean.

How a MENA builder picks and combines them

The stack also tells you what to build first. The rule, echoing Anthropic's own advice to start with the simplest solution and add complexity only when needed, is to climb the stack one layer at a time — and stop the moment a layer is enough.

Consider a few GCC examples:

A Riyadh e-commerce store drowning in product descriptions does not need an agent. It needs a strong saved prompt (layer 1), and probably a skill (layer 2) that encodes its brand tone and the required fields. If it wants every new SKU described automatically on upload, it wraps that in a workflow (layer 5). No autonomy required.

A Dubai real-estate brokerage wants its assistant to answer questions using live listings from its CRM. That is a connection problem: an MCP server (layer 3) that exposes the CRM. Only if the brokerage wants the assistant to independently research a client, cross-check listings, and draft a tailored shortlist does it need an agent (layer 4).

A Cairo marketing agency running the same campaign brief across ten clients wants consistency, not improvisation — so a workflow (layer 5) that chains a research prompt, a copy skill, and a formatting step will serve it better than a free-roaming agent.

The pattern is always the same: a single prompt, then a skill when you need consistent know-how, then an MCP server when the model needs to reach real data, then a workflow when the task repeats, and an agent only when the steps genuinely cannot be predefined. Reaching for an agent first is the most common and most expensive mistake we see.

A quick way to locate yourself on the stack: if you can write down the exact steps, you want a workflow, not an agent. If the model keeps getting the tone or format wrong, you need a skill, not a longer prompt. If it keeps saying it lacks information, you need an MCP server, not a smarter model. And if you genuinely cannot predict how many steps a task will take, that — and only that — is where an agent earns its cost.

The AI asset economy and the Arabic opportunity

Step back and a bigger shift comes into view. Once building blocks become standardized and portable — and both MCP and Agent Skills are deliberately built as open standards — they stop being one-off configurations and start behaving like a market. Assets get packaged, published, rated, reused, and traded. The same skill or MCP server can move between Claude, ChatGPT, Gemini, and Cursor. This is the beginning of an AI asset economy, and it rewards whoever organizes it well.

For the Arabic-speaking world, that is an unusually open door. Internet penetration across the region is among the highest anywhere — 99 percent in the UAE and over 92 percent in Morocco — while high-quality Arabic technical content stays thin. A vast, connected, AI-curious audience is being served almost entirely in a second language. The builders and curators who close that gap first — who localize, vet, and organize AI assets for this market — are not just translating documentation. They are claiming ground in an economy that is still being mapped.

Where to start

The five words were never really competing. They are layers, and once you can see the stack, the soup turns into a system: prompts carry your intent, skills carry your know-how, MCP servers carry your reach, agents carry the decisions, and workflows carry the repetition. Pick the lowest layer that solves your problem, and add the next one only when you have to.

If you want a vetted place to begin, the AI Khazna vault organizes assets along exactly these layers — so you can find the right building block for the job instead of guessing at the vocabulary.

Sources

Anthropic — Introducing the Model Context Protocol

Anthropic — Building Effective Agents

Anthropic — Equipping agents for the real world with Agent Skills

Agent Skills — Overview (open standard)

Google Cloud — What is Model Context Protocol (MCP)?

Model Context Protocol — Official documentation

DataReportal — Digital 2025: United Arab Emirates