At Tixu.ai, our core metric of success isn’t course completion; it’s what our students build in the real world. We believe that true learning is demonstrated through application.

Occasionally, a student project comes along that is so elegant, so effective, and so perfectly representative of our core philosophy that we feel compelled to share it. This is one of those stories.

We want to deconstruct an incredible AI system built by one of our members—a developer who automated a soul-crushing 10-hour weekly meeting and replaced it with a 15-minute, data-driven report.

This is a masterclass in moving beyond simple prompting and into the realm of true AI Systems Architecture.

The Problem: The Soul-Crushing “Voice of the Customer” Meeting

Like many companies, this student’s team had a weekly meeting to review user feedback. It was a painful, 2-hour session where they would manually read through hundreds of support tickets, chat logs, and feedback forms. With prep time, it consumed over 10 hours of valuable team resources every single week.

The process was slow, subjective, and always reactive. Actionable insights were buried under a mountain of noise.

The Student’s Insight: The Monolith vs. Microservice Approach

The student’s first attempt, like many, was to throw everything at a single, large prompt. The result was generic garbage. Then, applying the systems-thinking principles we champion at Tixu, they had a breakthrough. This isn’t a prompting problem; it’s an architectural problem. Instead of a single, monolithic AI, they decided to build a “cognitive assembly line”—a pipeline of small, specialized AI agents, each with a single responsibility.

The architecture they designed was brilliant in its simplicity:

The Classifier Agent (The Router): A cheap, fast model to do nothing but assign a category to each raw ticket.

The Structuring Agent (The Parser): Another cheap model to extract key information from the ticket and format it as clean JSON.

The Analyst Agent (The Brain): A powerful, expensive model that only receives the clean, pre-processed data to perform a high-level strategic analysis.

This is how they built it.

Step 1: The Classifier Agent (The Router)

The student’s first agent was a specialist in classification, built on a fast model like GPT-3.5-Turbo. Its system prompt is a masterclass in constraint.

# Conceptual Agent Class
class AIAgent:
def __init__(self, system_prompt: str, model: str):
self.system_prompt = system_prompt
self.model = model

def execute(self, user_data: str) -> str:
# Placeholder for actual API call
return f"SIMULATED_RESPONSE"

# The prompt tells the AI exactly what to do, and what NOT to do.
CLASSIFIER_PROMPT = """
You are a text classification microservice. Your ONLY function is to classify a user support ticket into one of the following categories: ['Bug Report', 'Feature Request', 'Billing Inquiry', 'Usability Feedback', 'Other'].
You must respond ONLY with the category name as a raw string. Do not add any explanation or conversational text.
"""

ClassifierAgent = AIAgent(system_prompt=CLASSIFIER_PROMPT, model="gpt-3.5-turbo")

Step 2: The Structuring Agent (The Parser)

This agent acts as the data sanitation layer. It takes the unstructured chaos of human language and turns it into clean, machine-readable JSON.

STRUCTURING_PROMPT = """
You are a data extraction microservice. You will receive a user support ticket. Your task is to extract the following information and return it as a JSON object with three keys: 'summary', 'feature_area', and 'sentiment'.

- 'summary': A one-sentence, neutral summary of the user's core problem or request.
- 'feature_area': The specific part of the application the user is talking about (e.g., 'File Upload', 'User Profile', 'Dashboard'). If not mentioned, use "Unknown".
- 'sentiment': Classify the user's sentiment as one of: ['Positive', 'Neutral', 'Negative'].

Respond ONLY with the JSON object.
"""

StructuringAgent = AIAgent(system_prompt=STRUCTURING_PROMPT, model="gpt-3.5-turbo")

Step 3: The Analyst Agent (The Brain)

Here, the student used a powerful model like GPT-4 Turbo. This agent doesn’t waste its expensive cycles on parsing. It receives a clean JSON array and does what it does best: high-level strategic thinking.

ANALYST_PROMPT = """
You are a Principal Product Analyst. You will be given a JSON array of structured support tickets from the past week. Your task is to perform a deep analysis and generate a concise, actionable executive summary in Markdown format.

The report MUST contain these three sections:

### 1. 🔥 Top 3 Urgent Bugs
Identify the most critical bugs. Prioritize based on frequency and impact on core functionality. For each, provide a one-line summary and the number of occurrences.

### 2. 🚀 Top 3 Strategic Opportunities
Analyze the feature requests and usability feedback. Identify the three opportunities with the highest potential business impact (e.g., improving retention, attracting a new user segment). Justify your choices.

### 3. 📉 Hidden Churn Indicators
This is the most critical analysis. Go beyond simple negative feedback. Identify subtle, recurring *themes* that suggest deeper user frustration or product flaws. Surface the "unknown unknowns" that could be silent killers of our growth.
"""

AnalystAgent = AIAgent(system_prompt=ANALYST_PROMPT, model="gpt-4-turbo")

Step 4: The Final Pipeline

The student then wrote a simple Python script to orchestrate this entire workflow, creating a true AI-powered ETL pipeline.

import json
from collections import defaultdict

def run_analysis_pipeline(raw_tickets: list[str]) -> str:
print("Initializing AI-ETL Pipeline...")

processed_tickets = []

# EXTRACT & TRANSFORM stage (using our cheap agents)
for ticket in raw_tickets:
# Simulate API calls
category = ClassifierAgent.execute(ticket)
structured_json_str = StructuringAgent.execute(ticket)
# In a real app, you would combine the results

print(f"Processed {len(raw_tickets)} tickets into structured data.")

# LOAD stage (feeding clean data to our powerful agent)
# final_input = json.dumps(processed_tickets, indent=2)
# report = AnalystAgent.execute(final_input)

report = "[Simulated Final Report from AnalystAgent]"
print("Insight generation complete.")
return report

The Result: A 10x Transformation

This system, built by a single developer, had a massive impact:

Efficiency: A 10-hour weekly meeting was replaced by a 15-minute automated report.

Speed: Critical bugs were flagged in near real-time instead of waiting a week.

Insight: The “Hidden Churn Indicators” section surfaced deep-seated UX problems the team had been completely blind to, directly leading to changes that improved user retention.

This is What We Mean by “Systems Thinking”

We are incredibly proud to showcase this project because it perfectly embodies the Tixu.ai philosophy.

The student didn’t succeed because they found a “magic prompt.” They succeeded because they learned to think like an architect. They deconstructed a problem, designed a multi-step solution, and used the right tool for each job.

This is the skill that separates the top 1% of AI operators from the rest. It’s not about learning tricks; it’s about learning a new way to solve problems. Our entire curriculum at Tixu.ai is designed as a training ground for this exact mindset. We don’t just teach you about AI agents in a video; our challenges force you to build them, test them, and deploy them to solve real-world problems like this one.

This project is the ultimate #TixuProof.

If you’re a developer who wants to move beyond simply using AI and start architecting with it, we invite you to explore the system-driven approach. This is the future.