If you use AI tools regularly but do not have a technical background, terms like “LLMs,” “AI agents,” “RAG,” and “agentic workflows” can sound intimidating.
They do not need to be.
The simple truth is this:
LLMs answer.
AI workflows follow steps.
AI agents make decisions toward a goal.
Once you understand that difference, it becomes much easier to see where AI is going and how businesses can actually use it.
This Friday, it is worth asking a practical question:
Is your business just using AI tools, or are you building AI systems that actually save time, capture leads, and improve follow-up?
Let’s break it down in plain English.
What Is an LLM?
LLM stands for Large Language Model.
Popular AI tools like ChatGPT, Google Gemini, and Claude are built on top of large language models. These tools are excellent at reading, writing, summarizing, drafting, editing, brainstorming, and answering questions based on the prompts you give them.
For example, you can ask an LLM to:
-Write an email
-Summarize a document
-Create a blog outline
-Explain a complex topic
-Draft social media posts
-Rewrite website copy
-Answer customer-service style questions
That makes LLMs extremely useful.
But by themselves, standard LLMs have two major limitations.
First, they usually do not know your private business data unless you connect it or provide it.
For example, if you ask a basic AI chatbot, “When is my next client appointment?” it cannot answer unless it has access to your calendar or CRM.
Second, LLMs are passive.
They usually wait for you to type a prompt. Then they respond. They do not automatically check your systems, update your CRM, notify your team, follow up with leads, or book appointments unless they are connected to a larger workflow.
That is where AI workflows come in.
What Is an AI Workflow?
An AI workflow is a structured process where AI follows a predefined path.
In simple terms, an AI workflow tells the system:
When this happens, do this next.
For example:
-When a website form is submitted, send an instant text reply.
-When someone misses a call, send a follow-up text.
-When a lead comes from Google Ads, add the contact to the CRM.
-When a customer asks a common question, have AI answer based on approved business information.
-When a new lead comes in, notify the business owner immediately.
This is where AI starts becoming more useful for real business operations.
A workflow can connect AI to tools like:
-Your website
-Your CRM
-Your calendar
-Your forms
-Your phone system
-Your email
-Your text messaging system
-Your Google Ads tracking
-Your customer database
A popular term you may hear is RAG, which stands for Retrieval-Augmented Generation.
That sounds more complicated than it really is.
RAG simply means the AI can look something up before answering.
Instead of guessing, the AI retrieves information from a source, such as your website, knowledge base, calendar, documents, CRM, or product list. Then it uses that information to give a better answer.
For a business, this matters because you do not want AI making things up. You want AI using your actual business information.
For example, an AI receptionist should know:
-Your services
-Your hours
-Your service areas
-Your pricing structure
-Your booking process
-Your FAQs
-Your phone number
-Your consultation process
-What information to collect from a lead
That is the difference between a random chatbot and a useful business workflow.
The Limitation of AI Workflows
AI workflows are powerful, but they are still usually controlled by human-made rules.
That means the workflow can only do what it was designed to do.
If you build a workflow to check a calendar, it can check a calendar.
If you build a workflow to send a missed-call text, it can send a missed-call text.
But if the workflow faces a new situation it was not designed for, it may fail or require human help.
That is where AI agents come in.
What Is an AI Agent?
An AI agent is different because it is designed to work toward a goal, not just follow one rigid path.
Instead of telling the system every single step, you give the AI agent an objective.
The agent then reasons through the task, chooses tools, takes action, reviews the result, and keeps working until it gets closer to the goal.
A simple way to understand it:
-An LLM gives an answer.
-An AI workflow follows instructions.
-An AI agent works toward an outcome.
For example, instead of telling an AI system:
Step 1: Read this form.
Step 2: Send this email.
Step 3: Add this tag.
Step 4: Notify this person.
You may give an AI agent a broader goal:
Help qualify this lead and move them toward booking a consultation.
The agent may then decide to:
-Read the lead’s message
-Identify what service they need
-Ask a follow-up question
-Send the business owner a summary
-Add the lead to the CRM
-Recommend the next step
-Trigger a booking link
-Follow up if the lead does not respond
That is a major shift.
The AI is no longer just responding. It is helping manage a process.
How AI Agents Reason and Act
Many AI agents are built around a simple idea: reason and act.
Reason means the AI thinks through what needs to happen.
Act means the AI uses tools to do something.
For example, an AI agent might reason:
-This person is asking about Google Ads management.
-They are probably a potential lead.
-I need to collect their business type, monthly ad budget, location, and phone number.
-Then I should notify the business and recommend a consultation.
Then it acts:
-It asks the right questions.
-It records the information.
-It sends a notification.
-It updates the CRM.
-It triggers follow-up.
The better the agent is designed, the more useful it becomes.
Why This Matters for Small Businesses
This matters because many businesses do not have a traffic problem only.
They have a response problem.
-They miss calls.
-They reply too slowly.
-They forget to follow up.
-They pay for Google Ads without proper tracking.
-They have forms that do not notify anyone properly.
-They have websites that do not convert.
-They have leads sitting in a CRM with no real follow-up system.
That is where AI workflows and AI agents can create real value.
For example, if a lead calls your business after hours, an AI receptionist or missed call text-back automation system can respond immediately.
If someone fills out a website form, AI can help notify your team, collect more details, and push the lead toward booking.
If someone clicks your Google Ad, your system can track the lead source and help you understand whether your ad spend is producing real opportunities.
This is not about using AI because it sounds trendy.
This is about using AI to fix real business leaks.
The Wrong Way to Use AI
The wrong way to use AI is to randomly add tools without a strategy.
That usually creates digital clutter.
-A chatbot here.
-A CRM nobody uses.
-A website form going nowhere.
-Google Ads with weak conversion tracking.
-A calendar link that is not connected to follow-up.
-A business owner still manually chasing every lead.
That is not real automation.
That is a mess with software attached to it.
The Right Way to Use AI
The right way is to start with the business problem.
Ask:
-Where are leads coming from?
-Where are leads being lost?
-How fast are we responding?
-Who follows up?
-What happens after the first call?
-Are calls, texts, forms, and chats tracked?
-Are we measuring which marketing channels produce real customers?
-Are we using AI to support the sales process or just to sound modern?
Once those questions are answered, the right AI system becomes much easier to design.
For some businesses, the first step may be a missed-call text-back system.
For others, it may be an AI receptionist.
For others, it may be a better website, stronger CRM automation, or better Google Ads tracking.
The point is simple:
AI should support the business process, not confuse it.
Summary: The 3 Levels of AI
Here is the clean breakdown:
Level 1: LLMs
You provide an input. The AI gives you an answer.
Example:
You ask ChatGPT to draft an email or summarize a document.
Level 2: AI Workflows
You provide an input. The AI follows a predefined process and may use tools.
Example:
A website lead comes in, the system sends an instant reply, adds the lead to the CRM, and notifies the business.
Level 3: AI Agents
You provide a goal. The AI reasons, acts, observes, and improves the process toward that goal.
Example:
An AI agent helps qualify a lead, decide the next step, trigger follow-up, and push the prospect toward an appointment.
Final Thought
LLMs are powerful, but they are only the starting point.
AI workflows make those tools practical.
AI agents take it further by helping manage outcomes.
For business owners, the real question is not whether AI is impressive. It is whether AI can help reduce missed opportunities, improve response time, organize leads, and create more booked appointments.
BES Digital Marketing helps local businesses build practical AI receptionists, AI workflows, smart websites, Google PPC systems, and CRM automation designed to turn more leads into real opportunities.
Call 702-849-2001 for a free consultation.
From LLMs to AI Agents: A Non-Technical Guide to AI Workflows
If you use AI tools regularly but do not have a technical background, terms like “LLMs,” “AI agents,” “RAG,” and “agentic workflows” can sound intimidating.
They do not need to be.
The simple truth is this:
LLMs answer.
AI workflows follow steps.
AI agents make decisions toward a goal.
Once you understand that difference, it becomes much easier to see where AI is going and how businesses can actually use it.
This Friday, it is worth asking a practical question:
Is your business just using AI tools, or are you building AI systems that actually save time, capture leads, and improve follow-up?
Let’s break it down in plain English.
What Is an LLM?
LLM stands for Large Language Model.
Popular AI tools like ChatGPT, Google Gemini, and Claude are built on top of large language models. These tools are excellent at reading, writing, summarizing, drafting, editing, brainstorming, and answering questions based on the prompts you give them.
For example, you can ask an LLM to:
Write an email
Summarize a document
Create a blog outline
Explain a complex topic
Draft social media posts
Rewrite website copy
Answer customer-service style questions
That makes LLMs extremely useful.
But by themselves, standard LLMs have two major limitations.
First, they usually do not know your private business data unless you connect it or provide it.
For example, if you ask a basic AI chatbot, “When is my next client appointment?” it cannot answer unless it has access to your calendar or CRM.
Second, LLMs are passive.
They usually wait for you to type a prompt. Then they respond. They do not automatically check your systems, update your CRM, notify your team, follow up with leads, or book appointments unless they are connected to a larger workflow.
That is where AI workflows come in.
What Is an AI Workflow?
An AI workflow is a structured process where AI follows a predefined path.
In simple terms, an AI workflow tells the system:
When this happens, do this next.
For example:
When a website form is submitted, send an instant text reply.
When someone misses a call, send a follow-up text.
When a lead comes from Google Ads, add the contact to the CRM.
When a customer asks a common question, have AI answer based on approved business information.
When a new lead comes in, notify the business owner immediately.
This is where AI starts becoming more useful for real business operations.
A workflow can connect AI to tools like:
Your website
Your CRM
Your calendar
Your forms
Your phone system
Your email
Your text messaging system
Your Google Ads tracking
Your customer database
A popular term you may hear is RAG, which stands for Retrieval-Augmented Generation.
That sounds more complicated than it really is.
RAG simply means the AI can look something up before answering.
Instead of guessing, the AI retrieves information from a source, such as your website, knowledge base, calendar, documents, CRM, or product list. Then it uses that information to give a better answer.
For a business, this matters because you do not want AI making things up. You want AI using your actual business information.
For example, an AI receptionist should know:
Your services
Your hours
Your service areas
Your pricing structure
Your booking process
Your FAQs
Your phone number
Your consultation process
What information to collect from a lead
That is the difference between a random chatbot and a useful business workflow.
The Limitation of AI Workflows
AI workflows are powerful, but they are still usually controlled by human-made rules.
That means the workflow can only do what it was designed to do.
If you build a workflow to check a calendar, it can check a calendar.
If you build a workflow to send a missed-call text, it can send a missed-call text.
But if the workflow faces a new situation it was not designed for, it may fail or require human help.
That is where AI agents come in.
What Is an AI Agent?
An AI agent is different because it is designed to work toward a goal, not just follow one rigid path.
Instead of telling the system every single step, you give the AI agent an objective.
The agent then reasons through the task, chooses tools, takes action, reviews the result, and keeps working until it gets closer to the goal.
A simple way to understand it:
An LLM gives an answer.
An AI workflow follows instructions.
An AI agent works toward an outcome.
For example, instead of telling an AI system:
Step 1: Read this form.
Step 2: Send this email.
Step 3: Add this tag.
Step 4: Notify this person.
You may give an AI agent a broader goal:
Help qualify this lead and move them toward booking a consultation.
The agent may then decide to:
Read the lead’s message
Identify what service they need
Ask a follow-up question
Send the business owner a summary
Add the lead to the CRM
Recommend the next step
Trigger a booking link
Follow up if the lead does not respond
That is a major shift.
The AI is no longer just responding. It is helping manage a process.
How AI Agents Reason and Act
Many AI agents are built around a simple idea: reason and act.
Reason means the AI thinks through what needs to happen.
Act means the AI uses tools to do something.
For example, an AI agent might reason:
This person is asking about Google Ads management.
They are probably a potential lead.
I need to collect their business type, monthly ad budget, location, and phone number.
Then I should notify the business and recommend a consultation.
Then it acts:
It asks the right questions.
It records the information.
It sends a notification.
It updates the CRM.
It triggers follow-up.
The better the agent is designed, the more useful it becomes.
Why This Matters for Small Businesses
This matters because many businesses do not have a traffic problem only.
They have a response problem.
They miss calls.
They reply too slowly.
They forget to follow up.
They pay for Google Ads without proper tracking.
They have forms that do not notify anyone properly.
They have websites that do not convert.
They have leads sitting in a CRM with no real follow-up system.
That is where AI workflows and AI agents can create real value.
For example, if a lead calls your business after hours, an AI receptionist or missed call text-back automation system can respond immediately.
If someone fills out a website form, AI can help notify your team, collect more details, and push the lead toward booking.
If someone clicks your Google Ad, your system can track the lead source and help you understand whether your ad spend is producing real opportunities.
This is not about using AI because it sounds trendy.
This is about using AI to fix real business leaks.
The Wrong Way to Use AI
The wrong way to use AI is to randomly add tools without a strategy.
That usually creates digital clutter.
A chatbot here.
A CRM nobody uses.
A website form going nowhere.
Google Ads with weak conversion tracking.
A calendar link that is not connected to follow-up.
A business owner still manually chasing every lead.
That is not real automation.
That is a mess with software attached to it.
The Right Way to Use AI
The right way is to start with the business problem.
Ask:
Where are leads coming from?
Where are leads being lost?
How fast are we responding?
Who follows up?
What happens after the first call?
Are calls, texts, forms, and chats tracked?
Are we measuring which marketing channels produce real customers?
Are we using AI to support the sales process or just to sound modern?
Once those questions are answered, the right AI system becomes much easier to design.
For some businesses, the first step may be a missed-call text-back system.
For others, it may be an AI receptionist.
For others, it may be a better website, stronger CRM automation, or better Google Ads tracking.
The point is simple:
AI should support the business process, not confuse it.
Summary: The 3 Levels of AI
Here is the clean breakdown:
Level 1: LLMs
You provide an input. The AI gives you an answer.
Example:
You ask ChatGPT to draft an email or summarize a document.
Level 2: AI Workflows
You provide an input. The AI follows a predefined process and may use tools.
Example:
A website lead comes in, the system sends an instant reply, adds the lead to the CRM, and notifies the business.
Level 3: AI Agents
You provide a goal. The AI reasons, acts, observes, and improves the process toward that goal.
Example:
An AI agent helps qualify a lead, decide the next step, trigger follow-up, and push the prospect toward an appointment.
Final Thought
LLMs are powerful, but they are only the starting point.
AI workflows make those tools practical.
AI agents take it further by helping manage outcomes.
For business owners, the real question is not whether AI is impressive. It is whether AI can help reduce missed opportunities, improve response time, organize leads, and create more booked appointments.
BES Digital Marketing helps local businesses build practical AI receptionists, AI workflows, smart websites, Google PPC systems, and CRM automation designed to turn more leads into real opportunities.
Call 702-849-2001 for a free consultation.
BES Digital Marketing