Beyond Solo Intelligence: How Agentic Workflows Are Redefining AI Collaboration

As technology evolves, the way we build and use artificial intelligence is changing. Traditionally, AI agents were designed to perform isolated tasks. Now, agentic workflows — a coordinated network of agents that work together dynamically — are gaining traction. In this article, we’ll explore the evolution, break down key differences, and see real-time examples using simple language and clear diagrams.

1. Understanding AI Agents

AI agents are individual programs or systems that perform specific tasks. They take an input, process it, and provide an output. Think of an AI agent as a specialized worker: it is highly focused on one role, such as answering questions or processing data.

Diagram: Simple AI Agent Architecture

+----------------+
   |   User/Input   |
   +-------+--------+
           |
           v
   +----------------+
   |   AI Agent     |
   | (Task Specific)|
   +-------+--------+
           |
           v
   +----------------+
   |    Output      |
   +----------------+

This diagram shows how a single AI agent takes an input, processes it, and produces an output.

2. Understanding Agentic Workflows