Artificial Intelligence is evolving fast. What once was limited to chatbots and predictive models is now turning into dynamic systems that can make decisions, adapt to real-time feedback, and interact with tools and environments almost like humans. At the core of this new wave are AI Agents—systems capable of pursuing goals autonomously by reasoning, acting, and learning.

This article is the first in a four-part series exploring the rise of agent-based AI. In this introduction, we’ll explain what AI agents are, how they differ from agentic workflows, and how to decide which architecture best fits your project.

What Is an AI Agent?

An AI Agent is a computational system that, powered by large language models (LLMs) or other AI techniques, takes charge of its own decision-making process. It selects and uses tools, processes information, and adapts its behavior to achieve a specific goal with minimal human guidance.

Unlike traditional rule-based systems or preprogrammed bots, AI agents can reason about situations, identify the best course of action, and adjust based on context. This makes them suitable for dynamic, multi-step tasks where outcomes aren’t always predictable.

For example, an AI travel agent could receive the instruction “Find me the best flight to Madrid in June,” then consult several APIs, compare routes and prices, and even ask the user follow-up questions to refine the selection—all without human intervention beyond the initial prompt.

AI Agents vs. Agentic Workflows

As we dive deeper into the world of autonomous systems, it’s important to distinguish between two main types of architectures: autonomous AI agents and agentic workflows.

Autonomous AI Agents

Autonomous agents are systems that take a single instruction and independently carry out the steps needed to complete a task. They combine reasoning, tool use, memory, and error handling to act in real time.

They are particularly useful when the task:

• Involves uncertainty or incomplete information.
  
• Requires creative or adaptive problem-solving.
  
• Benefits from dynamic responses to changing inputs or user feedback.
  

In this setup, the AI is not just generating outputs—it’s deciding what to do, how to do it, and when to ask for help.

Diagram of how an autonomous agent works

Agentic Workflows

Agentic workflows, on the other hand, are structured systems built around a predefined sequence of steps. They use tools like LLMs and APIs, but each action is explicitly defined by the workflow’s design.

These workflows may include:

• Prompt chaining (where the output of one step is passed as input to the next).
  
• Routing (conditional logic to decide which sub-process to activate).
  
• Tool orchestration (using different APIs in parallel or in sequence).
  

Workflows are best suited to tasks that:

• Follow a known and repeatable sequence.
  
• Require strict validation or formatting.
  
• Need transparency and control at each step.
  

For example, a workflow might generate a blog post, translate it into multiple languages, validate grammar, and publish it to a content platform—all in sequence, using clearly defined steps.

Diagram of an agentic workflow

How to Choose: When to Use Agents vs. Workflows

The decision between using an autonomous agent or an agentic workflow depends on the nature of the task, the desired level of control, and how much unpredictability the system needs to handle.

Use agentic workflows when:

• The task involves structured, repeatable steps.
  
• Predictability and traceability are critical.
  
• Human supervision is needed at specific points in the process.
  

Use autonomous agents when:

• The task is open-ended or changes based on context.
  
• Reasoning, exploration, or improvisation is needed.
  
• You want the system to proactively solve problems or escalate issues.
  

In many real-world applications, hybrid models are emerging, combining the clarity of workflows with the autonomy of agents. For instance, a sales support agent might follow a fixed workflow for responding to frequently asked questions but switch to autonomous behavior when dealing with unique or complex requests.

The Inner Workings of a Single AI Agent

To better understand how agents operate, let’s break down their core components:

• Task: The initial objective or instruction given to the agent. This could be as simple as “summarize this document” or as complex as “negotiate a price with a customer.”
  
• LLM: The large language model that powers the agent’s reasoning and natural language understanding. Examples include GPT-4o, Claude, or Gemini.
  
• Tools: External software or APIs the agent can use to complete tasks. These might include databases, search engines, messaging services, or third-party integrations.
  
• Memory: Information storage that allows the agent to recall previous actions, store relevant data, or track progress across multiple tasks.
  
• Response: The final output or action produced by the agent after evaluating its available data and tools.
  

Together, these components allow an agent to operate much like a human assistant: interpreting instructions, deciding on the next move, using external resources, and learning from results.

Multi-Agent Systems: When One Agent Isn’t Enough

Some tasks are too complex for a single agent to handle effectively. In those cases, developers can create multi-agent systems, often referred to as “crews.”

In this architecture, several specialized agents collaborate, each with a defined role. A coordinating agent (sometimes called a manager or supervisor) oversees the operation, assigns tasks, and integrates the results.

Example setup for a report-generation system:

• A research agent gathers relevant data.
  
• A writing agent generates the content.
  
• A review agent checks for clarity and grammar.
  
• A manager agent ensures everything is completed on time and compiled into a final document.
  

Each agent may have access to its own tools, memory, and prompt templates, but they can also share information and context through a central orchestration layer.

This modularity increases both flexibility and fault tolerance. If one agent fails or produces incomplete work, others can compensate or reprocess the output.

Final Thoughts

AI Agents and Agentic Workflows are more than just buzzwords—they represent a shift in how we design, automate, and scale intelligent systems. Whether you’re looking to streamline internal processes or build entirely new product experiences, understanding these foundational architectures is the first step.

At Zarego, we work closely with companies to explore how technologies like AI agents can transform their operations—unlocking efficiencies, enhancing decision-making, and opening the door to new business models. With years of experience in software architecture and innovation, we help our clients move from concept to implementation with clarity and confidence.

If you’re curious about how these tools can be applied to your business, get in touch with us. We’re always happy to explore new ideas.