If you’ve been online for the last year, you’ve heard the term “AI agent” thrown around constantly. Everyone’s building them. Everyone’s talking about them. But ask someone to explain what they actually are, and you’ll get a lot of different definitions.
Let us cut through the noise and explain it simply.
An AI agent is just an LLM that uses tools by itself, in a loop. That’s the entire definition. No magic. No mystery.
But understanding how that loop works (and more importantly, when you should actually use one) can save you thousands of dollars and countless hours of debugging.
Understanding AI Agents Through a Real-World Analogy
Forget the technical jargon for a moment. Think about the last time you had to troubleshoot something, maybe a broken car, a leaking pipe or a computer that won’t start.
Here’s what you did:
- You checked the battery – it was fine.
- You checked the starter – it was fine.
- You checked the electromotor – AH! That’s the problem.
- You fixed it.
- You tested it to make sure it worked.
Notice something important here: you didn’t know ahead of time how many steps you’d need. You didn’t know which part was broken until you checked it.
Each step informed the next one. You adapted based on what you found. That’s exactly how an AI agent works.
It doesn’t follow a predetermined script. It makes decisions based on what happens at each step, adjusts its approach and keeps going until the task is complete.
The 3 Essential Components of an AI Agent
For something to be a true AI agent, it needs three things working together.
1.Tools
Tools are the hands and feet of your agent. Without them, your LLM can only generate text, it can’t actually do anything in the real world.
Tools are functions the agent can call to interact with systems, like:
- Opening and controlling a web browser
- Reading and writing files
- Executing code
- Searching databases
- Making API calls
- Sending emails
- Analyzing data
Think of tools as giving your AI the ability to take action, not just suggest actions.
2. Memory
Memory is what separates an agent from just making random tool calls.
The agent needs to remember:
- What it already tried
- What worked
- What failed
- What information it collected
This memory allows the agent to build on previous steps instead of starting from scratch each time. It’s the difference between systematic troubleshooting and randomly pushing buttons.
Memory can be short-term (for the current task) or long-term (learned from previous tasks). Both are valuable.
3. A Loop
This is the secret sauce. This is what makes it an “agent”.
The loop looks like this:
- Perceive: Read the current situation and available information
- Think: Analyze what needs to happen next
- Act: Choose and use a tool
- Observe: Check what happened
- Decide: Determine the next step based on results
- Repeat until the task is complete
Without this loop, you just have a one-shot LLM call. With the loop, you have an autonomous system that can handle complex, multi-step tasks.
Types of AI Agents
Reflex Agents
The basic type of agents that operate based on a predefined set of rules to guide their decision making process similar to having a checklist, for each scenario.
For example, if you type in a question like “What’s the price of this phone?” the agent immediately responds with the price. It’s just following a simple rule—whenever someone asks about the price, it gives a pre-programmed response.
It doesn’t try to understand or change its approach; it just follows its checklist.
Goal Oriented Agents
They possess objectives they aim to accomplish by utilizing their knowledge to make choices that facilitate the achievement of their goals.
Now imagine a marketing AI Agent that’s designed to increase sales for a new product.
Its goal is to sell more of this product, so it tailors email campaigns, social media ads, and website content to convince people to buy. The AI focuses on how it can reach more customers and drive them toward purchasing that product, using different strategies like discounts or highlighting special features.
It’s always thinking about how to achieve the goal.
Utility Based Agents
They aim to determine the effective approach by evaluating various alternatives and selecting the optimal course of action based on their assessment of what is most beneficial or advantageous in a given situation.
Let’s say a company wants to know if it should spend more money on Instagram ads or email campaigns. This AI agent analyzes which option would bring in more sales based on past performance, customer behavior, and cost.
It chooses the one that will give the most benefit to the company, helping it get the best bang for its buck.
Learning Agents
These are the smartest. They get better over time by learning from their experiences.
Imagine a learning agent working for an online store. At first, it helps market products based on general rules, but over time it gets smarter. It starts learning which types of ads work best for certain customers, which products are popular during certain times of the year, and even which words in an email make people more likely to click “Buy.”
The more data it gathers, the better it gets at predicting what will increase sales, constantly improving its strategies.
A Real Example: Lead Research Agent in Action
Let us walk you through a realistic example that shows how all three components work together.
The Task: “Research the Head Coach of University X and find their validated email”.
Here’s how an AI agent handles this:
Step 1: The agent thinks: “I need to find their website first.”
It uses the web browser tool to search for “University X website”.
Step 2: The agent thinks: “I found their website. Now I need to check if they list their coaching staff”.
It uses a teammate, a specialized research agent (another AI agent that’s an expert at website analysis) to scan the site structure.
Why another agent? Because we have to protect the main agent’s context window (more about it in another post)
Step 3: The research agent reports back: “Found the Head Coach listed on the athletics staff page – John Smith”
Step 4: The main agent thinks: “Now I need to find and validate their email address.”
It uses another specialized agent focused on email finding and validation. This agent uses its own tools to find and validate emails like: Find Email, Verify Email, Web Browser tools
Step 5: The email agent reports: “Found and validated: john.smith@university.edu”
Step 6: The main agent compiles all the information into a structured report.
Task complete.
Notice what happened here:
- The agent didn’t know ahead of time which email pattern would work
- Each step built on information from the previous step
- If the website was down, the agent could pivot to LinkedIn
- If no email was found on the website, it could try other sources
- The agent used multiple tools and even other specialized agents
This adaptability is what makes it an agent, not a simple automation.
The Big Confusion: Agents vs Workflows
Here’s where most people get it wrong.
They slap the “AI agent” label on everything and wonder why their costs are exploding.
Let us be clear about the difference:
A Workflow is like following a recipe:
- Step 1: Boil water
- Step 2: Add pasta
- Step 3: Cook for 10 minutes
- Step 4: Drain
You know all the steps ahead of time. You do them in order. Nothing changes based on the results.
An Agent is like troubleshooting:
- You don’t know what you’ll find
- Each step depends on what you discovered in the last step
- You adapt your approach based on results
- You stop when the problem is solved, not after a fixed number of steps
Both are valuable. But they solve different problems.
When to Use a Workflow (Most of the Time)
- You know exactly what needs to happen
- The steps are always the same
- You want predictable costs
- You want easy debugging
- Speed and reliability matter more than flexibility
Example: Sending a welcome email when someone signs up. You always do the same steps. No decisions needed.
When to Use an AI Agent (Less Often Than You Think)
Use an AI agent when:
- You don’t know the steps ahead of time
- The task changes based on what you discover
- Each situation requires different approaches
- You need autonomous decision-making
- The task is genuinely too complex for a fixed workflow
Example: Researching and qualifying leads from diverse sources. Each lead requires different research paths.
The Real Cost of Using Agents (When You Don't Need Them)
Workflows are cheap. You pay for a fixed number of LLM calls. You know exactly what it costs.
Agents are expensive. Every decision point is another LLM call. The agent might make 5 calls, or it might make 50. You won’t know until it’s done.
We’ve seen companies spend 10x more on agents when a simple workflow would have worked better.
Why? Because agents sound cooler. Because everyone’s building them. Because they’re scared of being left behind.
But the thing is: the best engineers aren’t the ones using the fanciest technology. They’re the ones using the simplest tool that solves the problem.
The Debugging Nightmare
Let’s say something goes wrong.
Workflow breaks? Easy. You look at exactly which step failed and fix it.
Agent breaks? Good luck.
The agent might have made 15 autonomous decisions before it failed. Which decision was wrong? Was it step 3, or did step 3 work fine but step 9 used bad data from step 3?
Without proper observability tools, you’ll spend hours (or days) tracking down the issue.
You can’t just say “it failed at step 5” because there is no fixed “step 5.” The agent chose its own path.
When To Choose An AI Agent vs Workflow
Before you build an AI agent, ask yourself two questions:
Question 1: “Do I know all the steps ahead of time?”
- Yes? -> Use a workflow
- No? -> Maybe you need an agent
Question 2: “Will the steps change based on what I discover?”
- No? -> Use a workflow
- Yes? -> Maybe you need an agent
If you answered “workflow” to both questions, don’t build an agent just because it’s trendy.
Your future self will thank you.
How Cubeo AI Helps With Using AI Agents
We’re using AI agents to make life easier for developers and businesses. Here’s how:
- Custom AI Agents: Cubeo AI lets you create your own AI Agents. You can train it with your own data, like documents or videos.
- Tool Integration: Cubeo AI’s agents can use a variety of tools. They can search the web, use APIs, or even connect with other AI Agents.
- Automated Workflows: With tools like Zapier, Salesforce and Hubspot, Cubeo AI’s agents can automate complex tasks across different apps.
- Intelligent Research: Cubeo AI has a Researcher Agent that can dive deep into topics and create comprehensive reports.
- Code Generation: Using models like GPT 5.2 or Anthropic Claude 4.5 Sonnet Latest, Cubeo AI’s agents can help write and improve code.
- Data Analysis: With Elasticsearch integration, Cubeo AI’s agents can quickly find insights in large datasets.
- LinkedIn Integration: Cubeo AI has special tools for searching LinkedIn profiles and companies, making recruitment and sales easier.
- Web Scraping: Cubeo AI’s agents can gather information from any webpage, helping you stay up-to-date with the latest information.
Whether you’re a developer, a business owner, or just someone curious about AI, Cubeo AI has tools that can help you harness the power of AI agents.
Ethical Considerations
As AI agents become more powerful, we need to think about the ethical implications:
- Privacy: AI agents often need a lot of data to work well. We need to make sure this data is protected and used responsibly.
- Bias: AI agents can sometimes reflect the biases in their training data. We need to work hard to make them fair and unbiased.
- Few Job Positions Available: As AI agents take over some tasks, some job positions might disappear. Because AI could automate up to 80% of our work, there won’t be the same demand for some of them. We need to think about how to handle this change.
- Accountability: When AI agents make decisions, who’s responsible if something goes wrong? This is a tough question we need to answer.
- Transparency: We should be able to understand how AI agents make decisions, especially for important things.
Takeaway
What is an AI agent?
- An LLM + Tools + Memory + Loop
- It makes autonomous decisions based on results
- It’s like troubleshooting, not following a recipe
When should you use one?
- Only when you truly need adaptive, decision-making behavior
- Not when a simple workflow would work
- Not just because it sounds impressive
The 3 components:
- Tools (functions it can use)
- Memory (tracking what happened)
- Loop (keep going until done)
The reality:
- Agents are more expensive than workflows
- Agents are harder to debug than workflows
- Most problems don’t need agents
- Simple is better
Final Thoughts
We’re in the middle of an AI agent hype cycle. Everyone’s building them. VCs are funding them. LinkedIn is full of posts about them.
But the companies that will win aren’t the ones using the most complex AI.
They’re the ones who understand when to use an agent and when a simple workflow will do.
They’re the ones who ask “what’s the simplest solution?” before they ask “what’s the coolest solution?”
That’s the real skill: knowing which tool to use and when.
So before you architect your next AI agent system, take a step back.
Do you really need an agent? Or would a workflow do the job better, cheaper and more reliably?
The answer might save you a lot of time, money and late-night debugging sessions.
