What Does “AI-Ready” Really Mean?

Almost everyone is using AI at the moment.

LLMs are being integrated. Chat interfaces are being added. Systems generate SQL, write reports, and summarise data.

So far, so good. However, there is a fundamental confusion I keep encountering:

Using AI is not the same thing as designing a system that can genuinely work with AI.

To clarify this distinction, we need to define three concepts: Agent, Multi-Agent, and AI-Ready.

Agent

An agent is essentially a decision-making mechanism that performs a specific task. It can analyse text, generate a query, or recommend an action. When equipped with well-defined tools, it can be highly efficient.

Today, many systems operate at this level. There is a single agent with access to certain tools, and it executes the given task.

For simple and well-defined problems, this is entirely sufficient.

But as processes become more complex, a single-step decision mechanism begins to struggle.

Multi-Agent

Real-world processes are rarely linear. They are multi-step and feedback-driven. Data is collected in one place, analysed in another, evaluated elsewhere, and if necessary, the plan is revised.

This is where multi-agent systems come into play.

• One agent gathers data.
• Another analyses it.
• A third validates the output.

The process can be restructured dynamically when needed. The critical factor here is not the number of agents — it is the orchestration.

For a multi-agent system to function effectively, it requires an orchestration layer. This layer determines:

• Which agent is activated and when
• Which tool is used
• Whether the output is sufficient
• Whether the process should continue or be revised

In some scenarios, a single powerful agent is enough. However, as decision points increase and processes become more dynamic, a multi-agent approach becomes more scalable.

So, What Does “AI-Ready” Mean?

Many organisations say:

“We have APIs. We have REST services. We provide OpenAPI documentation. AI can call our services.”

Technically, that may be true.

But that does not mean the system is AI-Ready.

Let’s consider a simple example:

“Analyse sales performance, identify at-risk customers, and produce a summary.”

In traditional integrations, the calling system already knows:

• Which endpoint does what
• What each parameter means
• How the process should proceed

The flow is predefined and deterministic.

In LLM-based systems, however, you often provide only the objective.

The model must decide:

• Which services to call
• Which metrics define “risk”
• In what sequence the workflow should unfold

The task flow is formed dynamically.

If the system has not been designed to accommodate this dynamic behaviour, then technically AI may be integrated — but the system is not AI-Ready.

The Pillars of an AI-Ready System

To be AI-Ready, a system requires several foundational elements:

1. Discoverability

Tools and services must not only be callable — they must describe what they do in a way that is machine-understandable (this is crucial).

Input-output structures, business context, and expected impact must be explicit.

Providing a list of endpoints is not enough.

You need a layer of meaning.

And this meaning should not rely on internal business jargon alone — it must be described in a way an AI system can interpret. A term like “membership fee” or “dues”, for instance, can vary significantly depending on the domain.

2. A Semantic Layer

LLMs operate on concepts, not table names.

If the domain model is invisible, and if the data schema does not align with business concepts, the system may be technically accessible but semantically meaningless to AI.

An LLM cannot infer that tbl_cst_trx_01 actually represents “Customer Sales Transactions”.

In an AI-Ready system:

• The domain model is explicitly represented.
• There is a semantic bridge between raw data and business meaning.

3. Accessible Business Rules

If a “risk definition” exists only as embedded backend code, the LLM cannot reason about it.

Business rules should be modelled in a way that makes them:

• Explainable
• Referencable
• Callable when needed

4. Guardrails

Working with uncertainty does not mean working without control.

Authorisation boundaries, data access policies, and contextual tool usage rules must be clearly defined.

Dynamism and control should be designed together — not treated as opposing forces.

The Right Order of Things

In my view, the correct sequence is this:

1. First, design your system to be AI-Ready.
2. Then connect it — internally or externally — to a single-agent or multi-agent architecture.

When done the other way around, what usually happens is this:

• An LLM is introduced.
• Existing endpoints are stretched beyond their intended design.
• Gaps are patched through increasingly complex prompts.

The system appears to work.

But as complexity grows, fragility increases.

That is AI integration.

It is not AI architecture.

An integration-ready system is designed for predefined, deterministic workflows. It assumes prior developer knowledge.

An AI-Ready system, by contrast, is modelled to respond to goal-based requests. It is discoverable, semantically meaningful, open to dynamic task flows — yet controlled and observable.

AI is not merely a feature.

It is an architectural principle.

If your system is not AI-Ready, you may be using AI — but you are not truly building a system that works with AI.

Oh, and before I forget —

Emakin 9.0 is on its way.