We are going to build a customer support agent for a small electronics store. The agent will handle order lookups, answer policy questions, and hand off to a human when needed, all while keeping full conversation history. Because we are using Oxlo.ai's flat per-request pricing, long back-and-forth sessions cost the same as short ones, which makes this architecture affordable to run in production.
What you'll need
- Python 3.10 or newer
- The OpenAI SDK:
pip install openai - An Oxlo.ai API key from https://portal.oxlo.ai
- A virtual environment (optional but recommended)
Step 1: Configure the client
First, verify that the Oxlo.ai endpoint responds. I always run a single ping before adding logic.
from openai import OpenAI
client = OpenAI(base_url="https://api.oxlo.ai/v1", api_key="YOUR_OXLO_API_KEY")
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Say hello and confirm you are ready."},
],
)
print(response.choices[0].message.content)
Step 2: Design the system prompt
The system prompt is the only place where behavior is enforced. I keep it strict but short so the model does not drift.
SYSTEM_PROMPT = """You are Zed, a customer support agent for TechNova, an electronics store.
Your job is to help customers with order status, returns, and store policies.
Rules:
- Be concise and friendly.
- Never make up order details. Ask for an order ID if needed.
- If the user asks for a human representative, reply with exactly: [HANDOFF]
- Do not process refunds yourself. Offer the returns portal link instead.
- Do not provide legal, medical, or financial advice.
- Current date: 2025-01-15.
"""from openai import OpenAI
client = OpenAI(base_url="https://api.oxlo.ai/v1", api_key="YOUR_OXLO_API_KEY")
SYSTEM_PROMPT = """You are Zed, a customer support agent for TechNova, an electronics store.
Your job is to help customers with order status, returns, and store policies.
Rules:
- Be concise and friendly.
- Never make up order details. Ask for an order ID if needed.
- If the user asks for a human representative, reply with exactly: [HANDOFF]
- Do not process refunds yourself. Offer the returns portal link instead.
- Do not provide legal, medical, or financial advice.
- Current date: 2025-01-15.
"""
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": "What is your return policy?"},
],
)
print(response.choices[0].message.content)
Step 3: Build the conversation loop
Conversational AI needs memory. I store every turn in a messages list and send the full context each time.
from openai import OpenAI
client = OpenAI(base_url="https://api.oxlo.ai/v1", api_key="YOUR_OXLO_API_KEY")
SYSTEM_PROMPT = """You are Zed, a customer support agent for TechNova, an electronics store.
Your job is to help customers with order status, returns, and store policies.
Rules:
- Be concise and friendly.
- Never make up order details. Ask for an order ID if needed.
- If the user asks for a human representative, reply with exactly: [HANDOFF]
- Do not process refunds yourself. Offer the returns portal link instead.
- Do not provide legal, medical, or financial advice.
- Current date: 2025-01-15.
"""
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
while True:
user_input = input("User: ")
if user_input.lower() in ["exit", "quit"]:
break
messages.append({"role": "user", "content": user_input})
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=messages,
)
assistant_msg = response.choices[0].message.content
print(f"Zed: {assistant_msg}")
messages.append({"role": "assistant", "content": assistant_msg})
Step 4: Add tool use for order lookups
Trusting the model to hallucinate order details is dangerous. I give it a lookup_order function and let it decide when to call it.
import json
from openai import OpenAI
client = OpenAI(base_url="https://api.oxlo.ai/v1", api_key="YOUR_OXLO_API_KEY")
SYSTEM_PROMPT = """You are Zed, a customer support agent for TechNova, an electronics store.
Your job is to help customers with order status, returns, and store policies.
Rules:
- Be concise and friendly.
- Never make up order details. Use the lookup_order tool when a user provides an order ID.
- If the user asks for a human representative, reply with exactly: [HANDOFF]
- Do not process refunds yourself. Offer the returns portal link instead.
- Do not provide legal, medical, or financial advice.
- Current date: 2025-01-15.
"""
def lookup_order(order_id: str):
db = {
"ORD-001": {"status": "shipped", "item": "Wireless Headphones", "eta": "Jan 18"},
"ORD-002": {"status": "processing", "item": "USB-C Cable", "eta": "Jan 20"},
}
return db.get(order_id, {"error": "Order not found"})
tools = [
{
"type": "function",
"function": {
"name": "lookup_order",
"description": "Get order status by order ID",
"parameters": {
"type": "object",
"properties": {
"order_id": {
"type": "string",
"description": "The order ID, e.g. ORD-001"
}
},
"required": ["order_id"]
}
}
}
]
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
while True:
user_input = input("User: ")
if user_input.lower() in ["exit", "quit"]:
break
messages.append({"role": "user", "content": user_input})
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=messages,
tools=tools,
tool_choice="auto",
)
msg = response.choices[0].message
if msg.tool_calls:
# Append the assistant message that requested the tool
messages.append({
"role": "assistant",
"content": msg.content or "",
"tool_calls": [
{
"id": tc.id,
"type": tc.type,
"function": {
"name": tc.function.name,
"arguments": tc.function.arguments,
}
}
for tc in msg.tool_calls
]
})
tool_call = msg.tool_calls[0]
fn_name = tool_call.function.name
args = json.loads(tool_call.function.arguments)
if fn_name == "lookup_order":
result = lookup_order(args["order_id"])
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"name": fn_name,
"content": json.dumps(result),
})
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=messages,
tools=tools,
)
msg = response.choices[0].message
assistant_msg = msg.content
print(f"Zed: {assistant_msg}")
messages.append({"role": "assistant", "content": assistant_msg})
Step 5: Add guardrails and handoff
Production agents need hard stops. I block sensitive topics client side and watch for the handoff tag the model was instructed to emit.
import json
from openai import OpenAI
client = OpenAI(base_url="https://api.oxlo.ai/v1", api_key="YOUR_OXLO_API_KEY")
SYSTEM_PROMPT = """You are Zed, a customer support agent for TechNova, an electronics store.
Your job is to help customers with order status, returns, and store policies.
Rules:
- Be concise and friendly.
- Never make up order details. Use the lookup_order tool when a user provides an order ID.
- If the user asks for a human representative, reply with exactly: [HANDOFF]
- Do not process refunds yourself. Offer the returns portal link instead.
- Do not provide legal, medical, or financial advice.
- Current date: 2025-01-15.
"""
def lookup_order(order_id: str):
db = {
"ORD-001": {"status": "shipped", "item": "Wireless Headphones", "eta": "Jan 18"},
"ORD-002": {"status": "processing", "item": "USB-C Cable", "eta": "Jan 20"},
}
return db.get(order_id, {"error": "Order not found"})
tools = [
{
"type": "function",
"function": {
"name": "lookup_order",
"description": "Get order status by order ID",
"parameters": {
"type": "object",
"properties": {
"order_id": {
"type": "string",
"description": "The order ID, e.g. ORD-001"
}
},
"required": ["order_id"]
}
}
}
]
BLOCKED_TOPICS = ["legal advice", "medical advice", "financial advice"]
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
while True:
user_input = input("User: ")
if user_input.lower() in ["exit", "quit"]:
break
if any(topic in user_input.lower() for topic in BLOCKED_TOPICS):
print("Zed: I cannot help with that. I can assist with orders, returns, and store policies.")
continue
messages.append({"role": "user", "content": user_input})
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=messages,
tools=tools,
tool_choice="auto",
)
msg = response.choices[0].message
if msg.tool_calls:
messages.append({
"role": "assistant",
"content": msg.content or "",
"tool_calls": [
{
"id": tc.id,
"type": tc.type,
"function": {
"name": tc.function.name,
"arguments": tc.function.arguments,
}
}
for tc in msg.tool_calls
]
})
tool_call = msg.tool_calls[0]
fn_name = tool_call.function.name
args = json.loads(tool_call.function.arguments)
if fn_name == "lookup_order":
result = lookup_order(args["order_id"])
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"name": fn_name,
"content": json.dumps(result),
})
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=messages,
tools=tools,
)
msg = response.choices[0].message
assistant_msg = msg.content
if assistant_msg and "[HANDOFF]" in assistant_msg:
print("Zed: I am transferring you to a human representative now. Please hold.")
break
print(f"Zed: {assistant_msg}")
messages.append({"role": "assistant", "content": assistant_msg})
Run it
Save the final script as support_agent.py, replace YOUR_OXLO_API_KEY, and run python support_agent.py. Here is a sample session.
User: Hi, where is my order?
Zed: Hello! I can help with that. Could you provide your order ID?
User: ORD-001
Zed: Let me check that for you.
Zed: Your order for Wireless Headphones has shipped and is expected to arrive by Jan 18.
User: I need a human
Zed: I am transferring you to a human representative now. Please hold.
Next steps
Replace the hardcoded lookup_order dictionary with a real call to your CRM or e-commerce backend. Then add stream=True to chat.completions.create and iterate over chunks so the user sees words appear immediately instead of waiting for the full response.
