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Introduction

AI is evolving—from static, rule-based agents to autonomous systems that can reason, plan, and act. If you’ve heard terms like AI agents and Agentic AI and wondered what’s the difference, this blog is for you.

We’ll compare the two paradigms, and more importantly—show you live code using Amazon Bedrock and LangChain.

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What’s the Difference?

AI Agents

These are task-specific, rule-driven programs that respond to user inputs based on predefined logic. They do not plan, learn, or adapt. Think of them like an interactive FAQ bot or a form-filling assistant.

Agentic AI

Agentic AI systems go further. They understand goals, break down complex instructions into subtasks, and may even call external tools/APIs to gather info, compute, or act. It mimics how a human assistant would approach a problem.

AI Agent – Basic Streamlit Example

Let’s start with a simple AI Agent: a Travel Assistant that replies based on fixed logic.

Code

# travel_agent.py




import streamlit as st




st.title("Travel Assistant")




questions = [

    "What are the best hotels in Paris?",

    "How can I book a flight to New York?",

    "What is the weather in Tokyo?"

]




question = st.selectbox("Choose a question:", questions)




if question == questions[0]:

    st.write("Top-rated hotels in Paris include Ritz Paris and Le Meurice.")

elif question == questions[1]:

    st.write("You can book a flight using Kayak, Expedia, or Google Flights.")

else:

    st.write("You can check the weather in Tokyo on weather.com or AccuWeather.")

# travel_agent.py

import streamlit as st

st.title("Travel Assistant")

questions = [

"What are the best hotels in Paris?",

"How can I book a flight to New York?",

"What is the weather in Tokyo?"

]

question = st.selectbox("Choose a question:", questions)

if question == questions[0]:

st.write("Top-rated hotels in Paris include Ritz Paris and Le Meurice.")

elif question == questions[1]:

st.write("You can book a flight using Kayak, Expedia, or Google Flights.")

else:

st.write("You can check the weather in Tokyo on weather.com or AccuWeather.")

Agentic AI – Powered by LangChain + Amazon Bedrock

Let’s build an Agentic AI version of the same travel assistant. It will interpret goals, reason about steps, and respond dynamically using Bedrock’s foundation models like Claude or Titan.

Prerequisites

Make sure you have:

An AWS account with Bedrock enabled
AWS credentials configured
Python libraries installed:
pip install langchain boto3 botocore

Step 1 – Set Up Bedrock LLM

import boto3

from langchain.llms.bedrock import Bedrock




# Connect to Bedrock runtime

bedrock_client = boto3.client("bedrock-runtime", region_name="us-east-1")




# Create a Bedrock LLM instance (using Claude v2 for this example)

llm = Bedrock(

    client=bedrock_client,

    model_id="anthropic.claude-v2",

    model_kwargs={"temperature": 0.7, "max_tokens_to_sample": 500}

)

import boto3

from langchain.llms.bedrock import Bedrock

# Connect to Bedrock runtime

bedrock_client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Create a Bedrock LLM instance (using Claude v2 for this example)

llm = Bedrock(

client=bedrock_client,

model_id="anthropic.claude-v2",

model_kwargs={"temperature": 0.7, "max_tokens_to_sample": 500}

)

Step 2 – Load Agent Tools

from langchain.agents import initialize_agent, load_tools

from langchain.agents.agent_types import AgentType




# Load tools like calculator (more can be added)

tools = load_tools(["llm-math"], llm=llm)




# Initialize agent

agent = initialize_agent(

    tools=tools,

    llm=llm,

    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,

    verbose=True

)

from langchain.agents import initialize_agent, load_tools

from langchain.agents.agent_types import AgentType

# Load tools like calculator (more can be added)

tools = load_tools(["llm-math"], llm=llm)

# Initialize agent

agent = initialize_agent(

tools=tools,

llm=llm,

agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,

verbose=True

)

Step 3 – Give it a Goal

# Ask the agent to plan a travel itinerary

response = agent.run(

    "Plan a 3-day budget-friendly trip to Paris. Include hotel, flight, food, and local experiences."

)




print(response)

# Ask the agent to plan a travel itinerary

response = agent.run(

"Plan a 3-day budget-friendly trip to Paris. Include hotel, flight, food, and local experiences."

)

print(response)

Tip: Use Skyscanner and Google Flights to monitor deals.

The model breaks down the goal, reasons through tasks, and presents a complete plan. This is Agentic AI in action.

Key Differences

AI Agents:

Predefined logic
Fixed answers
No planning or dynamic behavior

Agentic AI:

Understands natural language goals
Can plan multi-step tasks
Uses tools or APIs to complete objectives

Conclusion

Agentic AI is more than a chatbot. It’s a framework where LLMs become proactive assistants, capable of understanding, reasoning, and executing.

With Amazon Bedrock + LangChain, you now have the power to build these intelligent systems securely and at scale.

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About CloudThat

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