Building an AI Agent Is Surprisingly Simple. Getting It Into Production Is Where Things Get Interesting

I've noticed that the conversation around AI agents has become unnecessarily complicated.

We're constantly seeing new agent frameworks, orchestration platforms, multi-agent architectures and increasingly elaborate diagrams explaining how AI systems should work.

If you're relatively new to AI development, you'd be forgiven for thinking that building an AI agent requires some fairly sophisticated engineering.

But when you strip away the terminology, the underlying concept is surprisingly simple.

In many cases, you're taking an existing large language model, giving it instructions, providing access to a few tools and wrapping some application logic around it.

That's essentially the foundation of an AI agent.

Of course, there's a significant difference between getting an agent to work and getting it to work reliably in production.

And I think that's where we should be spending more of our time.

What Actually Makes Something an AI Agent?

Let's start with the basics.

A traditional chatbot receives a question and generates a response.

An AI agent goes a little further.

Rather than simply telling you what to do, it can use tools to retrieve information, interact with other systems and perform actions on your behalf.

At its simplest, an agent consists of three things:

1. A model: The LLM that interprets the request and determines what to do next.
2. Instructions: The context, objectives and constraints that guide the model's behaviour.
3. Tools: Functions or APIs that allow the agent to interact with external systems.

There's usually some application code managing the interaction between these components.

And that's where things become interesting.

The agent doesn't necessarily need an elaborate architecture to be useful.

It needs a clearly defined job, access to the right capabilities and a mechanism for completing that job.

Let's Build a Simple Example

Imagine we want to create an AI assistant that manages customer support tickets.

A customer sends a message:

"I placed an order last week, but it hasn't arrived. Can you check what's happening?"

In a traditional application, we'd probably build a workflow that asks for the order number, queries the database and returns the delivery status.

With an AI agent, we can allow the model to interpret the request and decide which tools it needs.

We might give it access to three functions:

• findCustomerOrders(customerId)
• getDeliveryStatus(orderId)
• createSupportTicket(orderId, issue)

The model receives the customer's message and the descriptions of those functions.

It identifies that the customer wants to know where their order is.

It proposes a call to retrieve the customer's orders. Our application executes that function and returns the results.

The model then identifies the relevant order and requests its delivery status.

If there is an issue, it might propose creating a support ticket.

That's it.

We've created an agent capable of interpreting a natural-language request, retrieving information and taking an action.

We didn't need to train a model.

We didn't need a vector database.

We didn't need five specialised agents communicating with one another.

We connected an existing model to a handful of functions and implemented the logic required to manage the workflow.

Obviously, this example assumes we've already authenticated the customer and that our APIs enforce the necessary permissions.

But the underlying concept really is that simple.

The Agent Is Essentially an Application With an LLM Making Some of the Decisions

I think this is an important distinction, particularly for product managers and developers who are trying to understand agentic AI.

The LLM isn't magically interacting with your systems.

It receives information describing which tools are available, then generates a structured request to use one of them.

Your application decides whether that request is valid and whether the tool should be executed.

The result is returned to the model, which can then decide what happens next.

Conceptually, the process looks like this:

User request → LLM → Tool request → Application executes tool → Result returned to LLM → Next action or response

That's the basic agent loop.

Frameworks can make the loop easier to implement, particularly when dealing with state management, tool execution, retries and handoffs.

But they don't fundamentally change what is happening.

I think developers benefit from understanding this process before jumping into an orchestration framework.

Otherwise, it's easy to introduce complexity without really understanding what problem the framework is solving.

Do We Actually Need Multiple Agents?

This is where I'm a little sceptical of some of the current thinking.

There's a tendency to design multi-agent systems for problems that could quite comfortably be solved by one agent.

We create a research agent, a planning agent, an execution agent, a validation agent and sometimes even a supervisor agent to coordinate them all.

There are legitimate reasons for separating responsibilities.

Different agents might require different tools, security boundaries, specialised instructions or independent execution.

But multiple agents also introduce additional model calls, latency, cost and failure points.

An error made by one agent can become the context for another.

A supervisor may incorrectly interpret the output from a specialist.

And debugging a workflow involving multiple probabilistic systems is generally more difficult than debugging one.

Before adding another agent, I'd ask a simple question:

What problem does this additional agent solve that we couldn't address with a function, a deterministic workflow or clearer instructions?

If we can't answer that, perhaps we don't need another agent.

For many applications, a single model with the right tools and well-designed orchestration is a perfectly reasonable starting point. OpenAI's practical guide to building agents makes a similar case: maximise what a single agent can do before splitting the work across several.

Not Every Workflow Needs an Agent

There's another question that I think gets overlooked.

Does the task actually require an LLM to make decisions?

Suppose we want a system that sends a payment reminder seven days after an invoice becomes overdue.

We could build an AI agent that checks invoices, calculates due dates and decides when to send reminders.

Or we could use a scheduled job and a few lines of application code.

The second option is cheaper, more predictable and easier to test.

Where AI becomes useful is when we introduce ambiguity.

Perhaps the customer replies with an explanation, disputes a charge or asks for different payment terms.

Now we have unstructured information that needs interpreting.

An LLM can help understand the request and determine what options are appropriate.

Even then, the actual change to the payment terms should probably be handled by a controlled application workflow.

The point is that we don't need to make an entire process agentic simply because one part of it benefits from AI.

In fact, I'd argue that the best AI architectures will often use relatively small amounts of probabilistic reasoning surrounded by conventional software.

So Why Is Production So Much Harder?

Because demonstrations rarely expose the conditions under which systems fail.

We can build an agent that successfully completes a task ten times and feel reasonably confident that it works.

But what happens when the customer provides incomplete information?

What happens when the API is unavailable?

What happens when the model selects the wrong tool?

What happens when the same action is requested twice?

What happens when a malicious instruction is embedded in a document the agent retrieves?

And perhaps most importantly, what happens when the model confidently proposes an action that is completely wrong?

These aren't particularly unusual edge cases.

They're the kinds of situations we've been designing software to handle for decades.

The difference is that the decision-making component is now probabilistic.

Which means we need to think carefully about how we introduce predictability around it.

The Production Architecture Doesn't Need to Be Complicated

I would start with the same engineering principles we'd apply to any other application.

Clear separation of responsibilities

The model should handle tasks that benefit from language understanding and reasoning.

Application services should handle authentication, authorisation, business rules, data validation and transactions.

Constrained tool access

An agent shouldn't have unrestricted access to an entire database or application API.

Give it narrowly defined functions with the minimum permissions necessary to complete its task.

Structured inputs and outputs

Use schemas to validate tool arguments and responses.

This won't guarantee that the model has made the correct decision, but it can prevent malformed or unexpected data from reaching downstream systems.

Explicit execution controls

Never rely solely on an instruction such as "do not issue refunds above AED 500."

If that limit matters, enforce it in the software executing the refund.

Observability

Record the relevant model decisions, tool requests, validation results, failures and execution outcomes.

Be deliberate about what sensitive information is retained in logs.

If something goes wrong, we need enough information to understand why.

Testing and evaluation

Traditional unit and integration tests still matter.

But we also need evaluations that assess how the system behaves across realistic scenarios, ambiguous inputs, adversarial instructions and changes to the model or prompts.

And we should measure more than whether the model produced a plausible answer.

Did the system complete the task correctly? Did it choose appropriate tools? Did it avoid unauthorised actions? How often did it require human intervention?

These are much more useful production metrics.

Why Guardrails in the Prompt Aren't Enough

One mistake I frequently see in conceptual agent architectures is treating the system prompt as the primary security boundary.

The developer writes a detailed set of instructions telling the model which actions are permitted.

And then assumes the agent will reliably follow those instructions.

But a prompt is not an access control system.

Neither is an LLM-based evaluator.

Both can help guide or assess behaviour, but they shouldn't be the final authority for consequential actions.

This is closely related to my thinking around Evidence-Bound Action Governance.

The agent should be allowed to interpret information and propose an action.

But the application should independently determine whether that action is permitted.

For example, a customer service agent can propose a refund.

The execution service verifies the transaction, checks policy, confirms permissions and only then processes the payment.

This separation is particularly important when the model processes external content.

A retrieved webpage, customer email or uploaded document could contain malicious instructions intended to change the agent's behaviour.

We cannot assume that the model will always distinguish between legitimate instructions and untrusted content.

That's why the underlying permissions and execution boundaries matter.

Reliability Isn't the Same as Determinism

There's another distinction worth making.

We often talk about making AI systems more deterministic.

What we generally mean is making their behaviour more predictable and reliable.

But even if we use structured outputs, low-variance model configurations and carefully written instructions, the reasoning process can still produce unexpected results.

I don't think the objective should be to eliminate all variation from the model.

That would undermine some of the reasons we're using AI in the first place.

Instead, we should identify where variation is acceptable and where it isn't.

An agent might choose different wording when responding to a customer. That's usually fine.

It might take different paths to retrieve relevant information. That can be acceptable too.

But it shouldn't be able to exceed a transaction limit, access another customer's information or bypass an approval requirement.

Those are constraints we can enforce independently of the model.

The model can remain probabilistic while the system maintains deterministic boundaries around certain actions.

Of course, that doesn't guarantee the entire workflow will always produce the correct outcome.

It gives us a more controlled foundation to build on.

Start With the Simplest Architecture That Works

If I were building a production AI agent today, I wouldn't begin by selecting an agent framework or designing a multi-agent architecture.

I'd start with the problem.

What outcome are we trying to achieve?

Which part of the workflow actually requires reasoning?

What information does the model need?

Which tools should it be allowed to use?

What happens when it makes a mistake?

And how will we know whether the system is delivering value?

From there, I'd build the smallest version that can complete the workflow.

One model. A few clearly defined tools. Application-controlled execution. Appropriate guardrails. Good logging and a set of realistic evaluations.

Then I'd test it.

Not just whether it can complete the happy path, but how it responds when information is missing, tools fail, instructions conflict or users behave unpredictably.

Only when the limitations become clear would I consider adding more sophisticated orchestration.

There's no particular value in building a complex agent architecture if a simpler approach achieves the same outcome.

For a step-by-step version of this, from choosing the right business process to introducing autonomy progressively, see our guide at ROQ CX on building production-ready AI agents.

The Real Opportunity Isn't in Building Agents

What interests me most about AI agents isn't the underlying technology.

It's how they change the way we design applications and business processes.

For years, we've built software around predefined user journeys.

We create interfaces, workflows, forms and decision trees that guide people through processes.

Agents introduce the possibility of allowing users to express an outcome in natural language, with the system working out how to complete the necessary steps.

That's a meaningful shift.

But it doesn't remove the need for good product thinking or software engineering.

If anything, it makes them more important.

We still need to understand the user problem, design the experience, manage risk, validate outcomes and measure whether the solution is actually working.

The agent is simply another component in that system.

Building an AI agent is relatively easy. Building a dependable product around one is where the actual engineering begins.

And I think the sooner we stop treating agents as something fundamentally magical, the better we'll become at putting them to work.

If you want to go a step further, I've written about how to govern what agents are allowed to execute, not just what they say.