Lately, we hear two terms everywhere:
AI Agents and Agentic AI.
At first, they sound like the same thing.
And to make things more confusing, they are sometimes used interchangeably.
But there is a useful difference between them.
The simplest way to think about it is:
An AI agent is a component. Agentic AI is a way of designing AI systems to autonomously pursue goals.
Let’s understand that from a developer’s point of view.
First: What happens with a normal LLM?
Imagine you ask an LLM:
“Find me a good hotel in Paris under €150.”
A traditional LLM interaction looks something like this:
User Prompt
↓
LLM
↓
Text Response
The model receives your prompt and generates an answer.
Conceptually, the code is something like:
response = llm.generate(
"Find me a good hotel in Paris under €150"
)
print(response)
The problem is that the model may know how to talk about hotels, but it cannot necessarily check live prices, search booking websites, compare options, or make decisions based on new information.
It generates.
It does not necessarily act.
That changes when we introduce tools.
What is an AI Agent?
Imagine giving the model several functions:
tools = [
search_hotels,
check_price,
check_reviews
]
Now instead of asking the LLM to immediately answer the user, we give it a goal:
Find the best hotel in Paris under €150.
The model can now decide:
I need hotels first.
↓
search_hotels()
I received 20 hotels.
↓
I need their prices.
↓
check_price()
Some are above €150.
↓
Remove them.
Now I should compare reviews.
↓
check_reviews()
I have enough information.
↓
Return the best option.
This is much closer to an AI agent.
A simplified agent can be thought of as:
AI Agent
=
LLM
+
Instructions
+
Tools
+
State / Memory
+
Decision Loop
The LLM becomes the reasoning engine, while the application provides the capabilities.
The agent loop
One of the most important ideas behind agents is the loop.
Instead of:
Prompt → LLM → Answer
we start getting:
Goal
↓
Reason
↓
Choose an action
↓
Use a tool
↓
Observe the result
↓
Reason again
↓
...
↓
Finish
In simplified Python:
def run_agent(goal):
history = []
while True:
decision = llm(
goal=goal,
history=history,
tools=available_tools
)
if decision.type == "tool_call":
result = execute_tool(
decision.tool,
decision.arguments
)
history.append({
"action": decision,
"result": result
})
elif decision.type == "finish":
return decision.answer
Of course, real production agents are more complicated.
But this little loop explains a huge part of how they work.
The agent is continuously doing something similar to:
Reason → Act → Observe → Decide again.
So what makes AI “agentic”?
Now imagine a more complicated request:
“Build a landing page for my startup.”
A normal generative AI system might generate React code and stop.
An AI coding agent could do something more interesting:
Read the project
↓
Inspect package.json
↓
Understand the existing stack
↓
Create components
↓
Write files
↓
Run the build
↓
Build failed
↓
Read the error
↓
Modify the code
↓
Run the build again
↓
Success
Notice the important part.
The user did not explicitly say:
Read package.json, then inspect the components, then write the files, then run the build, then fix the errors.
The system discovered those intermediate steps itself.
That is where agency becomes important.
The AI is no longer only answering:
“What should I do?”
It is starting to answer:
“What should I do next to accomplish this goal?”
AI Agent vs Agentic AI
This is where I find the distinction useful.
AI Agent
An AI agent is usually a software component designed to pursue a goal.
For example:
Coding Agent
Goal:
Fix bugs in my application.
Tools:
- read_file()
- write_file()
- search_code()
- run_tests()
- run_command()
You could literally represent it in code:
coding_agent = Agent(
model=model,
instructions="Fix software problems",
tools=[
read_file,
write_file,
run_tests
]
)
That is an agent.
Agentic AI
Agentic AI describes the broader behavior or architecture of a system where AI has meaningful autonomy over how a goal gets completed.
For example:
Goal:
"Build an e-commerce application"
↓
Planning
↓
Understand project
↓
Choose what needs to happen
↓
Frontend → Backend → Database
↓
Run tests
↓
Did it work?
↙ ↘
No Yes
↓ ↓
Investigate Review
↓ ↓
Change plan Finish
↓
Try again
The system doesn’t simply follow one predefined sequence.
It can:
-
create a plan
-
use tools
-
keep track of state
-
evaluate results
-
react to errors
-
modify its plan
-
retry
-
continue toward the goal
That is what makes the system more agentic.
Agentic AI does NOT necessarily mean multiple agents
This is an important misconception.
People sometimes explain it like this:
AI Agent = one agent
Agentic AI = multiple agents
That explanation is easy, but it is not completely accurate.
A single powerful agent can still behave very agentically.
For example:
User Goal
↓
One Coding Agent
↓
Plan
↓
Read project
↓
Write code
↓
Run tests
↓
Analyze failure
↓
Change plan
↓
Fix code
↓
Test again
Only one agent exists here.
But the system still has significant autonomy.
A multi-agent architecture is simply one possible way of building an agentic system.
Where multi-agent systems fit
Instead of having one agent do everything, we could create specialized agents.
For example:
User Goal
↓
Manager Agent
↓
┌────────────┼────────────┐
↓ ↓ ↓
Frontend Backend Database
Agent Agent Agent
└────────────┼────────────┘
↓
Test Agent
↓
Review Agent
↓
Result
In code:
planner = Agent(...)
frontend_agent = Agent(...)
backend_agent = Agent(...)
database_agent = Agent(...)
tester = Agent(...)
reviewer = Agent(...)
The planner could break a large objective into tasks.
Each specialized agent handles part of the problem.
The tester checks the result.
The reviewer decides whether another iteration is necessary.
This is a multi-agent system, and it can be a very agentic architecture.
But again:
Multi-agent is an architecture. Agentic AI is a broader concept about autonomy and goal-directed behavior.
Workflow vs Agentic Workflow
I think this is one of the easiest ways to understand the difference.
Imagine this Python program:
data = research()
summary = summarize(data)
email = write_email(summary)
send_email(email)
There may be AI models inside every step.
But the developer already decided the exact workflow:
Research
↓
Summarize
↓
Write email
↓
Send
The AI does not decide what comes next.
That is primarily an AI workflow.
Now imagine instead that the system receives:
“Research the most interesting AI development this week and send me a useful summary.”
The system could decide:
I need recent information.
↓
Search.
Is this source reliable?
↓
Maybe.
Search another source.
↓
Do I have enough evidence?
↓
No.
Research more.
↓
Now compare the sources.
↓
Find the most important development.
↓
Write the summary.
↓
Send it.
The key difference is:
In a traditional workflow, the developer controls most of the path.
In an agentic workflow, the AI can control parts of the path.
That is the idea that helped me understand agentic systems the most.
Planning and replanning
Another important feature is the ability to change a plan.
Imagine an AI coding system receives:
“Add authentication to this application.”
It initially creates this plan:
1. Create users table
2. Build authentication API
3. Build login page
4. Add JWT authentication
5. Test everything
But after inspecting the project it discovers:
The application already uses Clerk.
A rigid workflow might continue with the original plan.
A more agentic system should react:
Observation:
Clerk already exists.
↓
Old plan is no longer appropriate.
↓
New plan:
1. Inspect existing Clerk configuration
2. Connect current UI
3. Protect backend routes
4. Test authentication
This ability to:
plan → act → observe → replan
is one of the most interesting properties of agentic systems.
Memory matters too
If an agent works through a complicated task, it needs to remember its state.
For example:
state = {
"goal": "...",
"current_plan": [],
"completed_tasks": [],
"tool_results": [],
"errors": [],
"important_context": []
}
Without state, the system could forget:
-
what the original goal was
-
what it already tried
-
what errors happened
-
what files it changed
-
which tasks remain
So modern agentic systems are not simply:
LLM + Prompt
They often look more like:
Goal
↓
LLM
↙ ↘
Memory Tools
↘ ↙
Planning
↓
Action
↓
Environment
↓
Observation
↓
Evaluation
↓
Continue or Finish
The evolution is easier to see as a spectrum
Instead of thinking of “agentic” as a strict yes-or-no category, I like thinking about increasing levels of agency:
Less autonomy
↓
Chatbot
↓
LLM + RAG
↓
LLM + Tool Calling
↓
AI Agent
↓
Agent + Planning
↓
Agent + Memory + Feedback
↓
Agent that can Replan and Retry
↓
Long-running Agentic Workflow
↓
Multi-Agent System
↓
More autonomy
The more the system can independently decide what action should happen next, the more useful the word “agentic” becomes.
The idea I would remember
If I had to reduce everything to three sentences:
Generative AI generates.
An AI agent can reason and act using tools to accomplish a goal.
Agentic AI is about designing systems where AI has enough autonomy to plan, act, observe, adapt and continue working toward an objective.
Or even more simply:
Generative AI:
"Ask me something."
AI Agent:
"Give me a task."
Agentic AI:
"Give me a goal."
The interesting part of the next generation of AI applications is therefore not only making models smarter.
It is building better systems around those models:
tools, memory, planning, evaluation, orchestration, permissions and feedback loops.
Because eventually, the question changes from:
“How good is the model at answering?”
to:
“How reliably can the system accomplish a goal?”