What is an AI Agent? The Difference Between Manual & Automated Workflow

This article aims to clarify the difference between a manual workflow, a rule-based (coded) automated process, and an intelligent workflow powered by an AI agent.

It’s designed for decision-makers who want a clear, practical understanding of how these approaches apply to their operations.

Before diving into the definition of an AI agent, it’s essential to understand the two types of workflows already present in most organizations. By comparing manual processes, rule-based automation, and intelligent agents, we can clearly see what sets this new generation of AI tools apart.

The framework below will help you identify where your organization stands today—and what becomes possible with AI.

A manual workflow relies entirely on human intervention at every step. Here’s a typical example in a manufacturing context:

“What’s the status of client X’s order?”

Steps:

• A team member sends the request to a colleague
• The colleague searches the ERP
• The data is incomplete → they dig through Excel files
• They manipulate the data
• They send a PDF by email
• End result: the information arrives… two days too late, and often outdated.

This type of workflow consumes time and resources, and creates delays that impact operations.

With automation, some tasks are handled through lines of code. This reduces human effort, but remains rigid and limited.

• If the data is available in the ERP → the system retrieves and sends it.
• If not, developers must code for every possible exception: missing files, incorrect formats, corrupted data, and more.

But in reality, trying to account for every edge case and condition across a company’s processes is nearly impossible. It quickly becomes complex, expensive, and fragile.

Result:
✅ Fewer manual steps
❌ But: exceptions drive up development costs and lead to frequent errors

An AI agent is an advanced form of intelligent automation. It acts as a digital brain, capable of operating like a human within a process.

It receives a request, processes it, searches for information, uses tools, and returns a response.

It relies on three core components:

• Memory: stores previous interactions or contextual history
• Data: connects to ERP, CRM, or other internal systems
• Tools: uses search engines, spreadsheets, OCR, internal/external APIs

The AI agent combines these elements to deliver the best possible answer in real time—even when data sources are fragmented or complex.

The diagram below illustrates how an intelligent workflow powered by an AI agent works, from receiving a request to delivering the final result.

Rule-based automation quickly reaches its limits in complex environments like manufacturing, logistics, or operations.

An AI agent allows you to move from rigid scripting to adaptive intelligence.

If you’re still relying on Excel files to repeatedly find answers… it might be time to explore what an AI agent can do for you.