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📋 Lesson Notes & Resources
⬇️ Download Full Code
🧠 What is an Agentic AI Engineer?
🎯 Definition
An Agentic AI Engineer is a developer who builds AI agents that can think, decide, and act independently to complete tasks.
🔄 Flow
User → AI Agent → Decision (LLM Brain) → Tools → Response
🧩 Responsibilities
- Build AI agents using LangGraph / LangChain
- Integrate LLMs like OpenAI, Gemini, Claude
- Connect APIs, databases, and AWS services
- Design decision-making workflows
- Create end-to-end systems (UI → API → Agent)
🧑💻 Example
User asks: "Get my order details"
Agent decides to fetch data from database and returns result.
⚙️ Skills Required
- Python
- APIs (FastAPI / Flask)
- LLMs (OpenAI, Gemini)
- LangGraph / LangChain
- AWS (Lambda, API Gateway, S3)
🚀 Difference
| Normal Developer |
Agentic AI Engineer |
| Writes fixed logic |
Builds decision-making systems |
| If-else conditions |
AI-based reasoning |
| Static apps |
Intelligent agents |
💻 Sample Agent Code
import json
from openai import OpenAI
from common.constants import CHAT_GPT_API_KEY
from prompts.function_selection_prompt import SYSTEM_PROMPT
def addition(a, b):
print("This is addition function")
print("Results: ", a + b)
def subtraction(a, b):
print("This is subtraction")
print("Results: ", a - b)
def multiplication(a, b):
print("THis is multiplication")
print("Results: ", a * b)
if __name__ == "__main__":
input = "Please do the subtract for these two values 10, 5"
client = OpenAI(api_key=CHAT_GPT_API_KEY)
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": input}
]
)
result = response.choices[0].message.content
parsed = json.loads(result)
print(parsed)
if parsed['function'] == 'addition':
addition(parsed['a'], parsed['b'])
elif parsed['function'] == 'subtraction':
subtraction(parsed['a'], parsed['b'])
elif parsed['function'] == 'multiplication':
multiplication(parsed['a'], parsed['b'])
else:
print("Please select proper process")