Building Agentic AI with Python, Langchain, and Ollama for eCommerce & Factory Automation

Artificial Intelligence has moved beyond passive Q\&A bots. Agentic AI is a new frontier — where AI systems don’t just respond, they reason, plan, and act autonomously. In this post, we’ll build a hands-on Agentic AI system using:

🧠 Langchain — to structure our agent’s logic, memory, and tools
🔓 Ollama — to run powerful open-source LLMs locally (like LLaMA3 or Mistral)
🏭 Custom tools — for eCommerce and Factory Automation use cases

🚀 What Is Agentic AI?

Agentic AI refers to systems that operate with goals, autonomy, and tool-using capabilities. Unlike basic chatbots, an agentic AI can:

Retain memory of past actions and conversations
Use external tools (e.g., databases, APIs, scripts)
Reason and plan steps to solve complex tasks
Take multiple actions toward achieving a goal

🧰 Tech Stack Overview

Component	Description
Python	Main language
Langchain	Agent framework (tool use, planning, memory)
Ollama	Local LLM runtime for open-source models
Custom Tools	Functions for eCommerce & factory scenarios

🧠 Agent Architecture

Here’s a high-level view of how our agent works:

graph TD
    A["User Query"] --> B["Langchain Agent"]
    B --> C["LLM via Ollama (e.g., LLaMA3)"]
    B --> D["Memory (ConversationBuffer)"]
    B --> E["Tool Selector"]

    E --> F["search_products()"]
    E --> G["track_order()"]
    E --> H["check_production_status()"]
    E --> I["log_factory_issue()"]

    F --> J["eCommerce Inventory"]
    G --> K["Order System"]
    H --> L["Factory Machine DB"]
    I --> M["Maintenance Logging System"]

    C -->|Plan & Reason| B
    D -->|Context| B

📦 Step 1: Define Tools

Each tool simulates a real-world function such as looking up products or checking a machine’s status.

# tools.py
from langchain.tools import tool

@tool
def search_products(keyword: str) -> str:
    return f"Found 3 products matching '{keyword}': A, B, C."

@tool
def track_order(order_id: str) -> str:
    return f"Order {order_id} is currently in transit."

@tool
def check_production_status(machine_id: str) -> str:
    return f"Machine {machine_id} is 87% complete with batch #42."

@tool
def log_factory_issue(issue: str) -> str:
    return f"Issue '{issue}' has been logged and assigned to a technician."

🤖 Step 2: Initialize the Agent with Langchain + Ollama

# agent.py
from langchain_community.chat_models import ChatOllama
from langchain.agents import initialize_agent, AgentType
from langchain.memory import ConversationBufferMemory
from tools import search_products, track_order, check_production_status, log_factory_issue

llm = ChatOllama(model="llama3")  # Run with: `ollama run llama3`

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

agent = initialize_agent(
    tools=[search_products, track_order, check_production_status, log_factory_issue],
    llm=llm,
    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
    memory=memory,
    verbose=True
)

💬 Step 3: Run the Agent

# main.py
from agent import agent

print("=== Agentic AI for eCommerce + Factory ===")
while True:
    query = input("You: ")
    if query.lower() in ['exit', 'quit']:
        break
    result = agent.run(query)
    print("Agent:", result)

🧪 Sample Interaction

You: Search for bluetooth speakers
Agent: Found 3 products matching 'bluetooth speakers': A, B, C.

You: Track order #99123
Agent: Order 99123 is currently in transit.

You: What's the status of machine A7?
Agent: Machine A7 is 87% complete with batch #42.

You: Log issue: Machine A7 overheating
Agent: Issue 'Machine A7 overheating' has been logged and assigned to a technician.