Modern operations teams deal with an overwhelming influx of unstructured data daily—ranging from customer support tickets to product reviews. Manually triaging these inputs slows down response times, but spinning up serverless infrastructure to process them with AI is traditionally a massive devops headache.
Because Cyberoak includes a native, pre-installed toolkit for modern enterprise engineering, you don't need to configure pip configurations, deploy Docker setups, or configure environment dependencies.
In this tutorial, we will write an automated AI classification agent. The script pulls mock text datasets, feeds them through OpenAI's gpt-4o API client for structured sentiment extraction, maps the outcomes into a highly scalable pandas DataFrame, and commits a clean operational tracker file into your workspace storage.
Pre-Installed Magic: Zero Infrastructure Setup
Instead of initializing virtual environments, this automation seamlessly links multiple pre-loaded Cyberoak modules:
1. openai (v2.44.0): Connects natively to OpenAI endpoints to execute language processing with high-performance configurations.
2. pandas (v2.3.3): Handles relational tabular compilation of the responses.
3. openpyxl (v3.1.5): Formats the final structural outputs into standard workbook formats for your non-technical stakeholders.
Designing for Cyberoak Orchestration
To build enterprise-ready loops on Cyberoak, our automated script follows three foundational guardrails:
* Decoupled API Keys: Rather than hardcoding authorization keys, credentials are dynamically injected straight into the engine via Cyberoak's global context manager variables.
* Batch Processing Control: Passing a structural Python array inside a data processing loop ensures rapid, structured compilation.
* Unified Error Safeguards: A clean fallback block ensures that even if an API transaction hits a rate limit, the pipeline saves your partial progress safely without fully crashing the task runner.
Let's look at how the execution logic operates step-by-step.
preview.py
import json
import logging
import pandas as pd
from openai import OpenAI
# Contextual credentials supplied dynamically by Cyberoak Key-Value Parameters
# Make sure OPENAI_API_KEY is configured in your Cyberoak platform parameters
client = OpenAI(api_key=OPENAI_API_KEY)
def analyze_feedback_with_ai(feedback_list: list) -> list:
"""Uses GPT-4o via the pre-installed openai client to extract structured sentiment."""
processed_records = []
for item in feedback_list:
try:
print(f"Processing Feedback ID {item['id']} via OpenAI...")
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "system",
"content": (
"You are an operational data assistant. "
"Analyze customer feedback. Return exactly a valid JSON object "
"with keys 'sentiment' (Positive, Neutral, Negative) and "
"'summary' (a concise one-sentence summary)."
),
},
{"role": "user", "content": item["text"]},
],
response_format={"type": "json_object"},
temperature=0.0,
)
# Parse out the structured response payload
result_json = json.loads(response.choices[0].message.content)
processed_records.append(
{
"Feedback_ID": item["id"],
"Raw_Text": item["text"],
"Sentiment": result_json.get("sentiment"),
"Summary": result_json.get("summary"),
}
)
except Exception as e:
logging.error(f"Failed to analyze item {item['id']}: {str(e)}")
# Graceful degradation fallback
processed_records.append(
{
"Feedback_ID": item["id"],
"Raw_Text": item["text"],
"Sentiment": "Error",
"Summary": f"Processing failed: {str(e)}",
}
)
return processed_records
if __name__ == "__main__":
# Mock source data (In a production flow, ingest this via database or webhook)
incoming_data = [
{
"id": 101,
"text": "The new feature dashboard is incredibly fast, but I hate the new button color.",
},
{
"id": 102,
"text": "The platform completely froze on me during checkout. This needs immediate fixing.",
},
{
"id": 103,
"text": "Is there an onboarding webinar scheduled for next Thursday? Just curious.",
},
]
print("Initializing Cyberoak Intelligent Automation Workflow...")
# Run AI evaluations
analysis_results = analyze_feedback_with_ai(incoming_data)
# Use Pandas to transform raw dict objects into a clean structure
print("Formatting outputs into Pandas...")
df = pd.DataFrame(analysis_results)
# Export utilizing the preloaded openpyxl sheet engine
output_report = "ai_sentiment_report_2026.xlsx"
print(f"Writing final workbook asset: {output_report}")
df.to_excel(output_report, index=False, engine="openpyxl")
print("Workflow execution successfully finalized!")