Building Production-Ready AI Agents: What Changes Beyond the LLM

Building an AI agent is surprisingly easy to demonstrate.

Give an LLM some tools, connect it to an API, add a system prompt, and you can quickly create something that looks impressive.

But getting that agent into a real enterprise environment is a completely different problem.

The question is no longer:

"Can the model answer the question?"

It becomes:

"Can the system safely make decisions and interact with business systems?"

That's where the architecture becomes important.

From chatbot to agent

A traditional chatbot might look like:

User
  ↓
LLM
  ↓
Response

An enterprise AI agent is closer to:

User
  ↓
Agent / Orchestrator
  ↓
Reasoning
  ↓
Tools
  ↓
Enterprise Systems
  ↓
Business Action

The agent might need to access:

• databases
• internal APIs
• ERP
• CRM
• WMS
• cloud services
• documents
• monitoring systems
• business workflows

The LLM is only one component.

The five layers I would consider

A production-oriented agent architecture can be divided into five layers.

1. Model layer

This is where the LLM lives.

The model is responsible for understanding the request, reasoning about the task and selecting appropriate actions.

But it shouldn't automatically have unrestricted access to everything.

2. Orchestration layer

The orchestrator decides:

• which tool to call
• in what order
• whether more information is required
• whether the task should be delegated
• when the workflow is complete

This becomes particularly important when a task requires multiple steps.

3. Tool layer

Tools provide controlled capabilities.

For example:

get_inventory()
get_sales_forecast()
create_purchase_order()
search_customer()
update_delivery_status()

The agent should interact with these capabilities through explicit interfaces rather than having unrestricted access to the underlying systems.

4. Enterprise integration layer

This is where things become complicated.

Real companies rarely have one system.

You may have:

ERP
WMS
CRM
Production System
Data Warehouse
Internal APIs
SaaS Applications

The agent needs a reliable way to access the right information.

5. Governance layer

This is the layer that is often underestimated.

You need to think about:

• authentication
• authorization
• audit logs
• data access
• human approval
• monitoring
• rate limits
• failure handling
• rollback

A successful AI agent isn't simply one that can perform an action.

It's one where you can understand why the action happened and who authorized it.

The biggest architectural mistake

I think one of the biggest mistakes is starting with:

"Let's give the AI access to our database."

Instead, start with:

"What business action should the AI be allowed to perform?"

Then work backwards.

For example:

Business Action
      ↓
Required Permission
      ↓
Required Tool
      ↓
Required Data
      ↓
Agent
      ↓
LLM

This makes the boundaries much clearer.

Where MCP fits

This is also why protocols such as the Model Context Protocol (MCP) are interesting.

Instead of creating a completely custom interface between every AI application and every tool, MCP provides a standardized way for AI applications to interact with tools and resources.

The important question for enterprise systems isn't simply:

"Can we connect our ERP to an AI agent?"

It's:

"How do we expose only the capabilities that the agent actually needs?"

That's a much more interesting engineering problem.

In the next article, I'll look specifically at MCP and enterprise tools, including where I think the protocol fits — and where it doesn't.

For background on our work around AI agents and business-system integration, see https://pionero.io/.