Artificial Intelligence is moving beyond conversation. Modern architectures focus on Agentic AI—autonomous agents capable of calling APIs, executing workflows, and solving complex problems with minimal human intervention. For developers, integrating these agents with existing enterprise backends is the next frontier.
The Architecture of an AI Agent
An agent is built on three key pillars: planning, memory, and tools. Tools are the interfaces the agent can execute—such as calling an SAP BTP service or triggering an automated email notification.
from langchain.agents import Tool, AgentExecutor, create_openai_fn_agent
from langchain_openai import ChatOpenAI
# Define a tool that executes an OData call to SAP
def get_sap_stock(product_id: str) -> str:
# Simulated SAP OData call
return f"Product {product_id} has 42 units in stock."
tools = [
Tool(
name="GetSAPStock",
func=get_sap_stock,
description="Query the SAP inventory database for a specific product ID."
)
]
llm = ChatOpenAI(model="gpt-4", temperature=0)
# Setup agent executor here...