AI is no longer an experiment or a buzzword as it is becoming core infrastructure for how modern software is built and operated.

As AI systems evolve from passive tools into systems that can reason, act, and make decisions, new terms like AI agents are appearing everywhere. Unfortunately, many of these terms are poorly defined and widely misunderstood.

This article aims to fix that.

We’ll start by clarifying what AI actually is, then build up to a clear and practical understanding of AI agents, how they work, what makes them different, and why they matter.

WHAT IS AI

According to IBM, Artificial Intelligence (AI) refers to technology that enables computers and machines to simulate human learning, comprehension, problem-solving, decision-making, creativity, and autonomy.

At its core, AI is not magical. It is a system that works by learning patterns from data, data created and accumulated by humans over time and using those patterns to make predictions, decisions, or generate outputs.

The “intelligence” we observe in AI systems is the result of mathematical models, data, and optimization techniques not consciousness, intent, or awareness.

While there are many forms of AI, most practical systems fall into a few key categories.

Rule-Based Systems

Rule-based systems are the simplest form of AI. They operate entirely on predefined rules written by humans.

They do not learn. They do not adapt. They only follow instructions.

Example:

IF the warehouse light is turned on -> say "HELLO"

These systems are predictable and reliable, but extremely limited.

Machine Learning Systems

Machine learning systems differ from rule-based systems in one important way: they learn patterns from data instead of relying solely on hand-written rules.

Rather than explicitly telling the system what to do, we allow it to observe behavior and infer patterns.

Example:

• When is the light turned on today?
• When is it turned off?
• What about the last 7 days?
• What patterns emerge?

Result:

• Light turns on around 7:30 AM
• Light turns off around 8:00 PM

From this learned pattern, the system can now make a recommendation:

IF light is turned on around 7:30 AM -> say "Hello, Good morning"

At its core its not understanding rather it is pattern recognition based on data.

Large Language Models (LLMs)

Large Language Models are a specific type of machine learning system trained on massive amounts of text data.

They learn how language works by predicting the next token (piece of text) in a sequence, based on the context provided so far.

Despite how human-like they may appear, LLMs do not think or understand in the way humans do. Their outputs are driven by probability, context, and learned patterns not intent or awareness.

What makes LLMs powerful is their ability to:

• reason over large contexts
• generate structured outputs
• adapt responses based on prior information

This idea of context is important, we’ll return to it shortly.

By default, an LLM is stateless, it has no memory unless one is deliberately provided.

Now that we have a clear understanding of what AI is, we can move forward and examine how AI agents emerge from these systems.

AI AGENTS (AGENTIC AI)

AI agents are one of the main reasons AI adoption has accelerated so quickly in recent years. They represent a shift from AI as a passive tool to AI as an active system that can operate toward a goal.

Importantly, AI agents do not give AI “human-like thinking.” Instead, they give AI the ability to act.

An AI agent is a system that can run autonomously, often continuously, and perform tasks on behalf of a user by deciding what actions to take and which tools to use. Rather than responding once and stopping, an agent operates in a loop, observing its environment, making decisions, taking actions, and updating its state until a goal is achieved.

This is what removes one of the biggest limitations of traditional AI systems.

Most AI systems are reactive: they wait for input, produce output, and stop.

AI agents, on the other hand, are goal-driven. They can plan, execute workflows, and adapt based on feedback, often without constant user intervention.

In simple terms, an AI agent is a continuous autonomous loop that works toward a defined objective.

At a high level, every AI agent is composed of three core building blocks:

• The AI Model => the decision-making engine (the brain)
• The Tools => the mechanisms for interacting with the world (the hands)
• The Orchestrator => the system that manages control flow, state, and execution (the nervous system)

We’ll break down each of these components in detail.

The AI Model (The Brain)

The AI model is the core decision-making component of an AI agent. As discussed earlier, it is the same AI system on its own which is a standalone model with no inherent awareness of the environment it operates in.

An AI model does not see files, understand systems, or know what is happening around it unless that information is explicitly provided. It only works with the input it receives and the context made available to it.

Modern AI models are trained on massive amounts of data and are capable of producing highly accurate outputs across a wide range of tasks. However, this capability is entirely dependent on the quality and completeness of the context they are given.

This is why context is critical.

For an AI agent to behave reliably, the model must be supplied with:

• the current state of the system
• relevant history or memory
• clear goals and constraints

Without sufficient context, the model can only guess.

With proper context, it can make informed decisions and select appropriate actions.

In practice, providing rich and accurate context is one of the most important steps in building a robust AI agent workflow.

Example: Language Learning Agent

Imagine we are building a product that helps users learn Portuguese.

To create an effective AI agent workflow, the AI model (LLM) must be provided with detailed and relevant context about each user. This context is typically collected during onboarding and updated over time as the user progresses.

Some useful contextual information might include:

• User’s primary language
• Whether the user has any prior experience with Portuguese or similar languages (e.g. Spanish)
• User type (student, professional, married, etc.)
• User age (to adjust explanations vs. direct instruction)
• Whether the user is a polyglot (has learned multiple languages before)

Now, consider a new user entering the system.

1. Current State of the System

• User is a first-time user
• No lessons completed
• No progress history

2. Relevant History or Memory

• Primary language: English
• No prior experience with Portuguese
• Familiar with Spanish: No
• Age: 35
• User type: Married professional
• Not a polyglot

3. Goals and Constraints

• Goal: Help the user build basic conversational Portuguese from scratch

• Constraints:

• Avoid assuming prior language knowledge

• Use clear explanations before introducing grammar rules

• Progress at a beginner-friendly pace

• Prefer practical, real-life examples over abstract theory

With this context, the AI model is no longer guessing.

It can now decide how to teach, what to teach next, and how fast to move all without being explicitly instructed at every step.

The Tools (The Hands)

While the AI model decides what should be done, tools are what actually do the work.

In an agentic system, tools are the mechanisms that allow an AI agent to interact with its environment. They execute actions based on the model’s decisions and have access to parts of the system the model itself cannot see or manipulate directly.

Most tools are backend executors. They can:

• read and write files
• fetch or store data
• call APIs
• generate documents
• trigger workflows
• send notifications or updates to users

The AI model produces structured decisions or instructions, but it is the tools that turn those decisions into real-world effects.

Example: Tools in a Language Learning Agent

Continuing with the Portuguese learning app example, the AI model might decide:

• what lesson to present next
• how difficult the explanation should be
• whether the user needs an example, exercise, or review

The tools then execute these decisions by:

• rendering lesson content in the UI
• generating personalized exercises
• storing progress in the database
• creating a tailored study plan
• generating a Notion document or downloadable guide for the user

In this setup, the AI model never directly modifies files, databases, or user interfaces.

It outputs decisions.

The tools carry them out.

A key principle

• Tools do not think.
• They do not reason.
• They only execute.

This separation is intentional and critical.

By keeping decision-making (the model) separate from execution (the tools), agent systems become:

• safer
• easier to debug
• easier to extend
• easier to control

The AI model chooses what to do.

The tools define what is possible.

The Orchestrator (The Nervous System)

If the AI model decides what should be done and the tools do the work, the orchestrator is what connects everything together.

The orchestrator is responsible for managing the agent’s control flow. It decides when the model should think, when tools should run, how results are evaluated, and whether the agent should continue, adjust, or stop.

After a tool executes an action, the orchestrator:

• inspects the result
• compares it against the user’s goal
• determines whether the outcome is satisfactory
• identifies errors or gaps
• feeds updated context back to the AI model

This loop is what gives an AI agent its sense of continuity and progress.

Because of this, the orchestrator is the heart of an AI agent.

A powerful model with a weak orchestrator will behave inconsistently, repeat mistakes, or fail silently. A well-designed orchestrator, even with a modest model, can produce reliable and predictable behavior.

What the Orchestrator Actually Does

In practice, the orchestrator is responsible for:

• managing the observe => decide => act => evaluate loop
• handling retries and failures
• enforcing constraints and stopping conditions
• updating memory and historical context
• determining what the model sees next

This is also where learning at the system level happens.

Not learning in the sense of retraining the model, but learning in how context, preferences, and outcomes are accumulated over time.

What ultimately differentiates one AI agent from another is not the model it uses, but how well its orchestrator manages this loop.

Example: Orchestration in a Language Learning Agent

Continuing with the Portuguese learning app example:

Assume the user provides feedback after the first lesson and indicates that they prefer responses delivered with a core Portuguese speaking accent.

The AI model does not update this preference on its own.

The tool does not understand user intent.

It is the orchestrator that captures this feedback and updates the agent’s historical context so future decisions reflect this preference.

After receiving the feedback, the orchestrator updates the agent state as follows:

1. Current State of the System

• User is not a first-time user
• One lesson completed
• Feedback received on lesson delivery style

2. Relevant History or Memory

• Primary language: English
• No prior experience with Portuguese
• Familiar with Spanish: No
• Age: 35
• User type: Married professional
• Not a polyglot
• Prefers responses delivered with a core Portuguese accent

3. Goals and Constraints

• Goal: Deliver future lessons using a core Portuguese speaking accent

• Constraints:

• Avoid assuming prior language knowledge

• Use clear explanations before introducing grammar rules

• Progress at a beginner-friendly pace

• Prefer practical, real-life examples over abstract theory

With this updated context, the next time the AI model is invoked, it does not need to be re-instructed.

The orchestrator ensures the preference is consistently applied across future interactions.

Bringing It All Together: How AI Agents Actually Work

An AI agent is not a single piece of intelligence. It is a system composed of distinct parts, each with a clear responsibility.

When these parts are combined correctly, they form a continuous loop that allows the agent to operate autonomously, adapt over time, and reliably work toward a goal.

At a high level, the flow looks like this:

The Orchestrator observes the current state

It gathers context: user data, system state, memory, and goals. 1.

The AI Model decides what to do next

Using the provided context, the model reasons and produces a structured decision or plan. 1.

The Tools execute the decision

Tools interact with the environment, updating data, generating content, calling APIs, or triggering workflows. 1.

The Orchestrator evaluates the outcome

It checks whether the action moved the system closer to the goal, identifies errors or feedback, and updates memory. 1.

The loop repeats

With updated context, the agent continues until the goal is met or a stopping condition is reached.

This loop is what transforms AI from a reactive system into an agentic one.

How to think of it

You can think of an AI agent like this:

• The AI Model decides what should happen
• The Tools make it actually happen
• The Orchestrator decides when to think, when to act, and when to stop

None of these components are sufficient on their own.

A powerful model without tools cannot act.

Tools without orchestration become brittle automation.

An orchestrator without a reliable model has nothing useful to execute.

It is the coordination between all three that creates a real AI agent.

Why This Is Important

As AI systems continue to evolve, the model itself will become less of a differentiator. Models will improve, commoditize, and be swapped out over time.

What will matter more is:

• how well context is constructed
• how safely tools are exposed
• how intelligently the orchestration loop is designed

In practice, the quality of an AI agent is determined less by how “smart” the model is, and more by how well the system around it is engineered.

Finally

AI agents are not intelligent beings.

They are disciplined systems.

They work because they:

• operate in loops
• act under constraints
• learn from feedback
• and are carefully orchestrated

If you’re currently building AI agents, focus less on swapping models and more on improving how context is constructed, tools are scoped, and orchestration is designed. That’s where most real-world failures and breakthroughs happen.