Turning support signals into prioritized, explainable decisions using AI.

AI Support Signal Triage Agent

This repository documents a human-in-the-loop AI agent designed to help support, product, and engineering teams triage incoming support signals in a structured, explainable way.

The focus of this project is decision quality and reasoning, not automation or production deployment.

Quick navigation

• 📄 Full workflow guide (PDF): docs/workflow-guide-v2.3.pdf
• 🧠 Agent prompt (copy/paste): agent/prompt.md
• 📊 Sample input data: data/raw_signals_sample.csv
• ✅ Example output: data/triage_output_example.csv
• 🧪 Run log (baseline): runs/run-001.md
• 🏗 Architecture notes: docs/architecture.md
• ⚖️ Design decisions: docs/decisions.md

Problem

Support signals arrive from many channels such as email, chat, app feedback, and reviews. They are often noisy, duplicated, and unstructured.

Teams spend significant time manually:

• Identifying what is urgent
• Grouping related issues
• Deciding who should act

This increases response time and raises the risk of missing critical incidents.

Solution

This project defines an AI Support Signal Triage Agent that:

• Classifies support signals into operational categories
• Assigns conservative P0–P3 priorities
• Detects duplicates and clusters related issues
• Recommends a clear next action and owning team
• Explains why each decision was made
• Produces an executive-ready daily digest

The agent is intentionally designed as decision support, not autonomous automation.

What this project is (and is not)

This is:

• A reusable AI reasoning workflow
• Human-in-the-loop by design
• Explainable and conservative
• Reproducible at zero cost

This is not:

• A production automation system
• A ticketing or auto-reply bot
• A framework showcase
• A commercial SaaS product

How to use this repository

You can reproduce the agent behavior with no paid tools:

1. Open a fresh ChatGPT conversation
2. Copy the agent prompt from agent/prompt.md
3. Paste the sample dataset from data/raw_signals_sample.csv
4. Run the instruction described in the workflow guide
5. Review the structured output and digest

Full step-by-step instructions are in docs/workflow-guide-v2.3.pdf.

Repository contents

• docs/ – Full workflow guide, architecture notes, and design decisions
• agent/ – The agent prompt treated as a specification, with a changelog
• data/ – Sample inputs and example outputs for reproducibility
• runs/ – Logged agent runs documenting changes, observations, and refinements

Author

Moin Shaikh
Business Analyst & Solutions Consultant

Date created: 2025-12-18
Date updated: 2025-12-23