From Non-Agentic AI to Agentic AI

Agentic AI

LLM

Contrast fixed AI pipelines with systems that can choose actions, use tools, observe results and decide what to do next.

Author

MLStation

Published

October 1, 2026

So far, every system we built follows a path that we designed in advance.

A prompt application:

Prompt → LLM → Response

A RAG application:

Question → Retrieve → Context → LLM → Response

These systems can be powerful, but the application determines the steps.

Agentic AI introduces a different idea:

What if the system can decide which action should happen next?

What you will learn

What we mean by non-agentic and agentic AI
The difference between a fixed workflow and a decision loop
Why tools matter
A simple agent-style example
When an agent is unnecessary

Two ways to plan a trip

Consider a travel assistant.

Fixed workflow

A programmer defines:

User request
   ↓
Search flights
   ↓
Search hotels
   ↓
Summarize
   ↓
Return answer

Every request follows the same route.

Agentic workflow

Instead, the system receives a goal:

Help me plan a three-day trip under $1,000.

It may reason operationally:

What do I need?
   ↓
Check flights
   ↓
Observe price
   ↓
Too expensive?
   ├── YES → try different dates
   └── NO  → check hotel
                  ↓
             Observe price
                  ↓
             Budget remains?
                  ↓
             choose next action

The important difference is not simply “more AI.” It is control over the next action.

Definition 1 (Agentic AI) In this introductory series, agentic AI refers to an AI application in which a model participates in deciding which actions or tools to use, observes the results, and can repeat this process toward a goal.

A non-agentic workflow follows application steps that are primarily predetermined by the developer.

Fixed pipeline vs. decision loop

Put the two architectures side by side.

NON-AGENTIC                         AGENTIC

Input                               Goal
  ↓                                   ↓
Step A                              Decide
  ↓                                   ↓
Step B                              Action
  ↓                                   ↓
Step C                              Observe
  ↓                                   ↓
Output                         Done? ──┬── No
                                      │
                                     Yes
                                      ↓
                                    Output

Mathematically, a fixed pipeline can be imagined as:

y = f_3(f_2(f_1(x)))

The sequence f_1 \rightarrow f_2 \rightarrow f_3 is predetermined.

An agentic loop is closer to:

a_t = \pi(g, s_t)

where:

g = goal,
s_t = current state or observations,
a_t = next action,
\pi = decision policy implemented partly or wholly using the model.

The chosen action can change as new information arrives.

A concrete example: weather question

User asks:

What is the weather in Austin today?

A plain LLM could generate an answer from its internal knowledge, but current weather requires fresh data.

A non-agentic application might always call a weather API first:

Question
   ↓
Weather API
   ↓
LLM formats result

An agentic application may have several tools:

TOOLS
├── weather(city)
├── calculator(expression)
└── search_documents(query)

The model receives the request and chooses:

"What is the weather in Austin today?"
                  ↓
        choose weather("Austin")
                  ↓
             observe result
                  ↓
              answer user

If the user instead asks:

What is 18% of 240?

the same system may choose the calculator rather than the weather tool.

A tiny agent loop

The following example does not depend on a specific LLM provider. It exposes the control pattern.

tiny_agent.py

import json
from pathlib import Path

def calculator(expression):
    # Teaching example only:
    allowed = set("0123456789.+-*/() ")
    if not set(expression) <= allowed:
        raise ValueError("Unsupported expression")
    return eval(expression, {"__builtins__": {}})

def choose_action(user_request):
    """
    Placeholder for an LLM decision.
    A real agent would ask the model for a structured action.
    """
    if "18%" in user_request and "240" in user_request:
        return {
            "tool": "calculator",
            "input": "0.18 * 240"
        }

    return {
        "tool": "none",
        "input": None
    }

request = "What is 18% of 240?"

action = choose_action(request)

if action["tool"] == "calculator":
    observation = calculator(action["input"])
    answer = f"The result is {observation}."
else:
    observation = None
    answer = "No tool was selected."

trace = {
    "request": request,
    "action": action,
    "observation": observation,
    "answer": answer,
}

print(answer)

Path("agent_runs").mkdir(exist_ok=True)
Path("agent_runs/run_001.json").write_text(
    json.dumps(trace, indent=2)
)

The hard-coded choose_action() is intentionally a placeholder. In the next chapter, an LLM will make that structured choice.

For now, notice the architecture:

Request
   ↓
Choose action
   ↓
Execute tool
   ↓
Observe result
   ↓
Produce answer

That is the skeleton of an agent.

RAG and agents are not opposites

RAG answers:

What information should I retrieve?

Agentic control answers:

What should I do next?

An agent can use retrieval as one of its tools:

                    Agent
                      │
          ┌───────────┼───────────┐
          ▼           ▼           ▼
      Retriever   Calculator   External API
          │           │           │
          └───────────┴───────────┘
                      │
                  Observations
                      │
                      ▼
                 Next decision

So RAG can become a component inside an agentic system.

When not to use an agent

Agentic architecture is not automatically superior.

Suppose the task is always:

Upload invoice
   ↓
Extract fields
   ↓
Validate schema
   ↓
Store result

A deterministic workflow may be easier to test, cheaper, faster and safer.

Do not add autonomy without a reason

Use agentic control when the next useful action genuinely depends on the request or on intermediate observations.

If the correct sequence is already known, a fixed workflow may be the better design.

A practical comparison

Table 1: The architectural distinction is primarily about control flow

Question	Non-Agentic	Agentic
Who chooses the workflow?	Mostly developer	Model participates in choosing next action
Path through system	Usually fixed	Can change dynamically
Tools	Called at predetermined steps	Can be selected based on state/request
Intermediate observations	Used by fixed logic	Can influence the next model decision
Testing	Often simpler	Requires testing decisions and tool use
Best fit	Predictable processes	Variable, multi-step goals

What should you save?

Agentic systems introduce a particularly important artifact: the execution trace.

agent_run/
├── goal.txt
├── system_prompt.txt
├── available_tools.json
├── actions.jsonl
├── observations.jsonl
└── final_response.txt

A useful trace answers:

What did the agent know?
What action did it choose?
Which tool did it call?
What did the tool return?
Why did another step occur?
What final answer was produced?

Without traces, debugging an agent can become guesswork.

The conceptual jump

An LLM generates.

A RAG system retrieves, then generates.

An agentic system can decide, act, observe and decide again.

Summary

Non-agentic systems generally follow workflows predetermined by the application.
Agentic systems allow the model to participate in selecting the next action.
The basic agent loop is decide → act → observe → repeat.
Tools give an agent capabilities beyond text generation.
RAG can be one tool or subsystem inside an agent.
Use a fixed workflow when the sequence is already known.
Save the agent’s action/observation trace so behavior can be reproduced and debugged.

Next: Agentic AI — Building Your First AI Agent