What Are AI Agents?

What Is an AI Agent?

An AI agent is an AI system that can pursue a defined goal by interpreting information, deciding what to do next, using tools or software, and taking actions with some degree of autonomy.

The key concept is agency.

A standard generative AI interaction typically produces an output.

You ask:

Summarize this report.

The AI summarizes the report.

An agent can potentially determine and execute multiple steps needed to accomplish a broader objective.

You might instead say:

Review our latest marketing report and identify anything the team should investigate.

An agent could potentially:

Access report → analyze results → compare data → identify anomaly → retrieve supporting information → summarize findings → create task

The user defines the destination.

The agent has some ability to determine the route.

How Do AI Agents Work?

Most AI agents combine several capabilities.

At a simplified level, an agent needs to understand a goal, determine an action, execute that action, observe what happened, and decide what to do next.

A basic agent loop might look like:

Goal → Plan → Act → Observe → Evaluate → Act Again → Complete

Imagine an agent is given this goal:

Research three competitors and summarize their pricing.

The process might look like:

1. Interpret the objective.
2. Identify the competitors.
3. Search for their websites.
4. Visit pricing pages.
5. Extract pricing information.
6. Compare the results.
7. Notice that one price is missing.
8. Search for additional information.
9. Create a comparison.
10. Return the completed research.

That ability to respond to what happens during the process is an important difference between an agent and a simple predefined automation.

What Are the Main Parts of an AI Agent?

AI agents can vary substantially, but several components commonly appear.

AI Model

The underlying AI model provides language understanding and reasoning capabilities.

It helps the agent interpret:

• Goals
• Instructions
• Information
• Tool outputs
• Previous actions

Large language models can provide this reasoning layer for many modern agents.

Instructions or Goal

The agent needs to know what it’s trying to accomplish.

That might be narrow:

Categorize incoming support tickets.

Or broader:

Research potential topics for next month’s content calendar.

The broader the objective, the more decisions the agent may need to make.

Tools

Tools allow the agent to interact with systems beyond the AI model itself.

An agent might be able to:

• Search the web
• Read files
• Send email
• Update a CRM
• Query a database
• Create documents
• Update spreadsheets
• Use APIs
• Create calendar events
• Run code

Without tools, an AI system can primarily generate information.

Tools allow it to do things.

Memory and Context

An agent may need information from earlier steps.

That can include:

• Previous messages
• Customer information
• Task history
• Tool results
• Preferences
• Prior decisions

Memory helps the agent avoid treating every action as completely independent.

Planning

Some agents can break larger objectives into smaller tasks.

For example:

Goal: Research a competitor

could become:

Identify company → find products → find pricing → review positioning → find customer reviews → summarize findings

The plan may also change as new information becomes available.

Feedback

Agents need to understand whether an action succeeded.

For example:

Attempt action → receive result → evaluate result → decide next step

This feedback loop allows an agent to continue working instead of simply generating one response.

AI Agents vs. Generative AI

Generative AI and AI agents aren’t the same thing.

Generative AI creates content or information. AI agents use AI to pursue goals and take actions.

Generative AI can still be part of an agent.

In fact, it often is.

An agent might use generative AI to write an email during one step of a much larger workflow.

AI Agents vs. AI Automation

This is probably the most important distinction.

AI automation follows a defined workflow containing AI capabilities. AI agents have more freedom to determine which actions should be taken to accomplish a goal.

For example:

AI Automation

Lead submitted → AI analyzes lead → CRM updated → email drafted → salesperson notified

The workflow is already mapped.

The AI performs particular tasks inside it.

AI Agent

Goal: Qualify and follow up with new leads.

The agent might:

1. Retrieve new leads.
2. Review each lead.
3. Research the company.
4. Check previous interactions.
5. Determine qualification.
6. Select an appropriate follow-up.
7. Update the CRM.
8. Create a task for sales where necessary.

The agent determines more of the process itself.

That’s why I’d understand what AI automation is before getting too deep into agents.

AI automation gives you the foundation.

Agents add autonomy.

AI Agents vs. Workflow Automation

Traditional workflow automation is even more structured.

A workflow might be:

Form submitted → create CRM record → send confirmation email

Every step is predetermined.

An AI agent might receive:

Process this new customer inquiry.

Then determine what processing the inquiry actually requires.

Here’s the progression:

That doesn’t mean the bottom row is automatically better.

Sometimes you want predictability.

If someone submits an order, you probably don’t need an AI agent creatively deciding whether the order should be entered into your database.

Regular automation is often better for predictable processes.

Growth Gary’s guide to what workflow automation is covers that foundation in more detail.

What Can AI Agents Do?

AI agents can potentially support any process where software needs to interpret information and take multiple actions.

Common applications include research, marketing, sales, customer service, software development, and operations.

Research Agents

A research agent might:

Receive topic → search sources → read information → compare findings → identify gaps → conduct additional searches → summarize results

This can be useful when research requires multiple steps rather than one search.

Marketing Agents

A marketing agent might:

• Research competitors
• Analyze campaign performance
• Identify content opportunities
• Draft campaign assets
• Review customer feedback
• Recommend next actions
• Update marketing systems

For example:

Goal: Find content opportunities around AI visibility

An agent could potentially:

1. Review existing content.
2. Research competing websites.
3. Identify missing topics.
4. analyze search questions.
5. Recommend articles.
6. Create preliminary briefs.

That doesn’t mean you should publish whatever it produces.

It means the agent can perform more of the repetitive research process.

Sales Agents

Sales agents can potentially:

• Research prospects
• Qualify leads
• Summarize accounts
• Draft outreach
• Update CRM records
• Schedule follow-ups
• Prepare sales representatives for meetings

A useful agent might prepare the salesperson rather than trying to replace the salesperson.

Customer Service Agents

Customer service is another obvious application.

An agent could:

1. Receive a customer question.
2. Determine what the customer needs.
3. Search the knowledge base.
4. Check account information.
5. Generate a response.
6. Perform an allowed action.
7. Escalate when necessary.

The important part is step seven.

Good agents need to know when not to continue autonomously.

Coding Agents

Software development has become one of the most visible applications of agentic AI.

Coding agents can potentially:

• Inspect codebases
• Write code
• Find bugs
• Run tests
• Interpret errors
• Modify files
• Test again

Instead of merely generating a code snippet, the agent can interact with the development environment and iterate toward an objective.

What Are Marketing AI Agents?

Marketing AI agents are agents designed to accomplish marketing-related goals.

They might operate across:

• Content
• SEO
• Email
• Social media
• Advertising
• CRM
• Analytics
• Research

Imagine giving an agent:

Prepare this week’s newsletter.

It could potentially:

Review recent content → identify best stories → summarize articles → draft newsletter → select links → prepare subject lines → place draft in email platform

Compare that with simply asking ChatGPT:

Write a newsletter.

The second request generates content.

The first describes a process.

That’s the difference.

For the content-generation part specifically, Growth Gary’s guide to what AI writing is explains how generative AI can help create written marketing content.

Can AI Agents Create Content?

Yes.

Content creation can be one action within an agent workflow.

For example:

Research topic → identify audience questions → create outline → draft article → generate social posts → prepare newsletter summary

The agent might use AI writing during several of those steps.

Similarly, agents could incorporate AI-generated:

• Images
• Video
• Audio
• Emails
• Social content

Growth Gary’s guides to AI design and AI video explain those individual generation technologies.

An agent can potentially connect them into a larger production workflow.

That’s an important distinction:

AI tools create assets.

AI agents can coordinate tasks around those assets.

Can AI Agents Send Emails?

Potentially, yes.

If an agent has access to an email platform, it could potentially:

• Read incoming messages
• Categorize emails
• Draft responses
• Retrieve customer information
• Send messages
• Schedule follow-ups

But giving an agent permission to draft an email and giving it permission to send an email are two different risk levels.

That’s where human approval becomes useful.

For example:

Lead received → agent researches lead → agent drafts response → human approves → email sends

Once that process is reliable, some businesses may choose to automate lower-risk messages further.

If email is a major part of the workflow, understanding what email marketing is and how AI is used in email marketing provides useful context.

Are AI Agents the Same as Chatbots?

No.

A chatbot primarily communicates with users through conversation.

An AI agent can potentially take actions.

A basic chatbot might answer:

Your order is expected Tuesday.

An agent might:

Check order → discover delay → check replacement options → update shipment → notify customer

The line is becoming less obvious because modern AI assistants increasingly include tools and agentic capabilities.

But conversational ability alone doesn’t make something an agent.

The key question is:

Can the system act toward a goal, or does it only respond?

Are AI Agents Autonomous?

Somewhat.

“Autonomous” can be misleading because AI agents exist on a spectrum.

An agent might be allowed to:

Suggest only

or:

Draft but require approval

or:

Take predefined low-risk actions

or:

Operate independently across several tools

The amount of autonomy should depend on the consequences of getting something wrong.

I wouldn’t think about agents as either:

autonomous / not autonomous

I’d think about:

How much autonomy should this particular task receive?

That’s a much more useful business question.

What Are the Benefits of AI Agents?

The potential advantage of agents is that they can automate outcomes rather than individual tasks.

Agents Can Handle Multi-Step Work

Traditional AI tools usually require the user to repeatedly direct the next step.

Agents can potentially continue through a process.

Agents Can Reduce Tool Switching

A person might normally:

Open email → copy information → open CRM → search website → open spreadsheet → update record → send message

An agent connected to those systems can potentially coordinate several of those actions.

Agents Can Respond to Changing Information

Unlike a rigid automation, an agent may be able to change its approach based on what it discovers.

Agents Can Scale Knowledge Work

Tasks involving research, classification, summarization, and communication become easier to process at larger volumes.

Agents Can Work With Existing Software

Agents become particularly useful when they can interact with the systems businesses already use.

That’s where agentic AI starts becoming more than an interesting chatbot demo.

What Are the Risks of AI Agents?

The same thing that makes agents interesting makes them risky.

They can act.

Agents Can Make Bad Decisions

AI isn’t perfectly reliable.

An agent can misunderstand information or select an inappropriate action.

Agents Can Compound Errors

If an incorrect assumption affects the next five steps, the final result can become increasingly wrong.

Tool Access Creates Risk

An AI that can draft an email has limited power.

An AI that can:

• Send emails
• Delete files
• Modify databases
• Make purchases
• Change customer records

has considerably more.

Permissions matter.

Agents Can Be Difficult to Debug

If a traditional workflow breaks at step four, you inspect step four.

If an agent independently chose six different actions based on its interpretation of a situation, understanding exactly why it failed can be more difficult.

Security Becomes More Important

Agents may interact with external content and internal systems.

That creates risks involving:

• Data exposure
• Malicious instructions
• Unauthorized actions
• Incorrect permissions
• Prompt injection
• Sensitive information

More autonomy should come with more controls.

What Is Human-in-the-Loop AI?

Human-in-the-loop means requiring human review or approval at important points in an AI process.

For example:

Agent researches lead → drafts email → HUMAN APPROVES → email sends

Or:

Agent reviews refund request → recommends refund → HUMAN APPROVES → transaction processed

This can be one of the best ways to introduce agents into a business.

Let AI do the time-consuming preparation.

Let people retain control over consequential decisions.

As reliability improves, lower-risk steps can potentially become more autonomous.

What Tools Can AI Agents Use?

Agents become more useful when they can interact with other systems.

Depending on the platform, an agent might connect to:

• Email
• Calendars
• CRMs
• Spreadsheets
• Databases
• Browsers
• Search engines
• Project management tools
• Marketing platforms
• Ecommerce platforms
• APIs
• Internal knowledge bases

Workflow platforms can also become part of the infrastructure connecting AI with business applications.

That’s one reason tools such as Zapier, Make, and n8n increasingly overlap with the agent ecosystem.

Growth Gary’s best workflow automation tools guide covers that layer separately.

Do AI Agents Need APIs?

Not necessarily, but APIs are one common way agents interact with software.

An API allows one system to request information or perform an action in another system.

For example, an agent could use an API to:

Retrieve customer → update CRM → create task

Modern agent platforms may hide much of this complexity behind prebuilt integrations.

So a marketer doesn’t necessarily need to understand API programming to use AI agents.

But the underlying connections still matter.

Do AI Agents Need Coding?

Not always.

Some agents can be configured using natural-language instructions, visual workflow builders, and prebuilt integrations.

More sophisticated agents may require:

• APIs
• Code
• Databases
• Custom tools
• Authentication
• Infrastructure

The technical requirements depend entirely on what you’re trying to accomplish.

Don’t start by asking:

How do I build an AI agent?

Start with:

What process am I trying to improve?

Then decide whether an agent is actually necessary.

How Do You Build an AI Agent?

The safest way to start is narrow.

1. Define One Goal

Bad:

Handle our marketing.

Better:

Research incoming leads and prepare a qualification summary.

Specific objectives are easier to test.

2. Define What Information the Agent Needs

What does it need access to?

Perhaps:

• Lead form
• Website
• CRM
• Company information
• Qualification criteria

Give it the minimum information necessary.

3. Define the Tools It Can Use

Maybe it needs:

• Web search
• CRM read access
• CRM update access

It probably doesn’t need access to your accounting software.

Use minimum necessary permissions.

4. Define What It Can Do

Separate actions into categories.

Allowed automatically:

• Research
• Summarize
• Classify
• Draft

Requires approval:

• Send email
• Modify important records
• Make purchases
• Delete information

This creates guardrails.

5. Test the Agent

Use realistic scenarios.

Test:

• Normal cases
• Missing information
• Contradictory information
• Strange requests
• Failed tools
• Edge cases

Don’t only test the example you designed it around.

6. Measure Performance

Track:

• Accuracy
• Completion rate
• Time saved
• Error rate
• Human review time
• Business outcomes

Then expand autonomy only when the results justify it.

Should Small Businesses Use AI Agents?

Potentially.

But most small businesses probably shouldn’t start by building elaborate autonomous agent systems.

Start with a real repetitive problem.

For example:

Every lead requires 10 minutes of research before a sales call.

That’s a strong candidate.

An agent might prepare:

• Company overview
• Website summary
• Relevant products
• Potential needs
• Recent news
• Suggested questions

The salesperson still conducts the conversation.

The agent removes preparation work.

That’s useful.

Building a 14-agent “AI workforce” because somebody on YouTube said businesses without one will disappear by Thursday is probably less useful.

Are AI Agents the Future of Automation?

They’re likely to become an important part of it.

Traditional automation is excellent when processes are predictable.

AI automation expands that to processes requiring interpretation.

Agents go another step by allowing software to determine more of the path toward an objective.

So the progression looks something like:

Manual work → Workflow automation → AI automation → AI agents

But those stages aren’t replacements.

Businesses will continue using all four.

Sometimes a person should do the work.

Sometimes a simple rule is enough.

Sometimes AI should interpret something.

And sometimes an agent can handle a broader objective.

The real skill will be knowing which one to use.