Understanding AI Agents: Beyond Traditional Software |

To understand AI agents, let's first look at how they differ from traditional software.

The Limits of Traditional Software

Traditional software is built using programming languages and follows a fixed set of instructions. It takes an input, processes it in the same way every time, and produces a predictable output. It can interact with external systems like databases, file systems, or APIs, but always in a deterministic, predefined way.

The Rise of Generative AI

Compare that to generative AI applications powered by large language models like ChatGPT, Claude, Gemini, or DeepSeek. These are advanced systems that take input in natural language or even in other data types like voice, images, or structured data. They pass that input through a neural network and generate a response. Unlike traditional programs, they don't always act the same way; they're not deterministic.

These one-shot interactions are incredibly useful, but they come with major limitations. Their knowledge is frozen at the time they were trained, and they can't interact with their environment the way traditional software does.

What Are AI Agents?

That's where AI agents come in. By giving LLMs access to external systems and data, agents unlock a much wider range of real-world applications.

In simple terms, an AI agent is a digital system that operates autonomously within an environment. It usually performs three core functions:

Perceive the environment by accessing data, sensors, or inputs.
Decide using an internal reasoning engine to plan actions toward a goal.
Act by using tools to perform tasks in the real world.

This is called the perceive-decide-act loop. Think of a smart robot vacuum: It perceives the world using sensors, it decides how to navigate a room, and it acts by moving and vacuuming.

The idea of agents isn't new. In fact, it was a major research topic in the '90s and early 2000s. But back then, we didn't have reasoning models that were smart enough to handle complex problems. LLMs change that; they unlock new levels of reasoning, planning, and context understanding.

How Do Modern Agents Work?

At its core, an AI agent allows generative AI models to interact with external tools. This interaction is achieved by an orchestration or engine component that manages the agent's instructions and goals, tool calling, and optionally gives it access to short- and long-term memory.

These tools allow the agent to interact with their environment. For example, they can:

Read and write data from files or databases
Search the web or interact with online forms
Call APIs
Access codebases to generate or update software
Communicate with physical devices like cameras, smart sensors, and other hardware

When Should We Use Agents?

Agents are ideal when we need autonomy, complex reasoning, tool use, and adaptability. Think of these processes, for example: customer support, sales funnels, employee hiring, etc. These are examples of complex use cases that can't be fully automated by classical approaches. They require a level of intelligence that is better suited for AI agents.

In the industry, various frameworks like LangChain, LlamaIndex, AutoGen, CrewAI, and Pydantic AI make it easier to build agents.

The Challenges Ahead

This is very exciting, but it's not without challenges.

Unpredictability: LLMs are powerful but unpredictable. They can hallucinate facts or misuse tools in ways that cause system failures.
Cost: LLMs are computationally expensive, and multi-step planning increases both runtime and cost. If an agent takes numerous steps to solve a problem, that cost adds up very quickly.
Security Risks: Giving agents access to real systems, databases, devices, and user accounts introduces serious risks. What if an agent deletes critical data or leaks private information?