🎯 Support Triage Agent

World-class multi-domain AI support triage system

Reads a CSV of support tickets → classifies, prioritises, responds, and escalates — with 18 production-grade intelligence features built in.

Features · Quick Start · Architecture · Outputs · VS Code Guide

🧠 What It Does

Given a CSV of support tickets across HackerRank, Claude (Anthropic), and Visa, the agent:

Identifies the request type (bug / product issue / feature request / invalid)
Classifies the product area and company domain
Assesses urgency (P0–P3), sentiment, and churn risk
Decides whether to reply directly or escalate to a human agent
Retrieves grounded evidence from the support corpus using BM25 + TF-IDF hybrid search
Generates a safe, grounded, tone-personalised response
Enriches every decision with 18 intelligence signals
Exports 6 output files including a visual HTML dashboard

Works completely offline without an API key. Optionally uses Claude Sonnet for LLM-quality responses.

✨ Features

Core Pipeline

Feature	Description
🔍 BM25 + TF-IDF Retrieval	Hybrid lexical search with per-company sub-indices
🛡 Two-Layer Safety Screen	Rule-based pre-screen + LLM post-validation
🏢 Multi-Domain Support	HackerRank, Claude, Visa — plus company inference for unlabelled tickets
⚡ Grounded Responses	All answers cite the support corpus — no hallucination
🔄 LLM + Offline Modes	Runs with Claude Sonnet API or fully offline

Intelligence Features (18 total)

#	Feature	What It Does
1	🧠 Confidence Scoring	Per-decision confidence [0–1] with retrieval quality and classification certainty
2	🚨 Incident Outbreak Detector	Clusters related tickets, detects platform outages, drafts mass response
3	🎭 Sentiment Analyser	Detects angry / frustrated / distressed / neutral / positive with intensity
4	⏱ SLA Priority Queue	Auto-assigns P0 (<1h) → P3 (<72h) with domain-aware urgency rules
5	📚 Corpus Gap Detector	Finds KB blind spots, suggests articles to write, tracks coverage rate
6	✅ Response Quality Validator	5-dimension self-validation: relevance, groundedness, completeness, safety, actionability
7	🌍 Multilingual Threat Detector	Catches injections in French, Spanish, German, Arabic, Chinese, Base64, Leetspeak
8	📊 Analytics Dashboard	Terminal dashboard + 33-column analytics CSV for BI tools
9	🔐 PII Auto-Redactor	GDPR/PCI-DSS compliance — redacts card numbers, Aadhaar, PAN, emails, API keys
10	💰 Churn Risk Scorer	0–100 churn probability with revenue-at-risk tier and retention priority
11	🎨 Tone Personalizer	Adapts response register: Technical / Business / Non-Technical / Student / Enterprise
12	🔁 Deduplication Engine	TF-IDF cosine similarity — finds near-duplicate tickets, prevents double-handling
13	📖 Auto-FAQ Builder	High-confidence resolutions become draft FAQ entries (Markdown + JSON)
14	🔏 Compliance Audit Trail	SHA-256 chained, tamper-evident log of every decision (SOC 2 / GDPR ready)
15	💡 Prevention Advisor	"How to prevent this next time" tips appended to successful resolutions
16	❤️ Customer Health Score	0–100 composite score: sentiment + urgency + confidence + quality + churn
17	🌐 HTML Executive Dashboard	Self-contained single-file dashboard with Chart.js charts and sortable table
18	⭐ VIP Account Detection	Identifies enterprise, high-volume, churn-risk, and executive-contact tickets

🚀 Quick Start

# 1. Clone the repository
git clone https://github.com/your-username/support-triage-agent.git
cd support-triage-agent

# 2. Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Add your tickets
#    Place support_tickets.csv in support_issues/
#    Required columns: issue, subject, company

# 5. Run
python code/run_agent.py

That's it. No API key required — the agent runs in Grounded mode out of the box.

🖥 Running in VS Code

Step 1 — Open the project

File → Open Folder → select the support-triage-agent folder

Step 2 — Open the terminal

View → Terminal   (Ctrl+` on Windows/Linux, Cmd+` on Mac)

Step 3 — Create a virtual environment

# Mac / Linux
python3 -m venv .venv
source .venv/bin/activate

# Windows
python -m venv .venv
.venv\Scripts\activate

You'll see (.venv) in your terminal prompt when active.

Step 4 — Install dependencies

pip install -r requirements.txt

Step 5 — (Optional) Set your Anthropic API key

Without a key → Grounded mode (offline, zero cost, fully functional)
With a key → LLM mode (uses Claude Sonnet, best quality)

# Copy the example env file
cp .env.example .env              # Windows: copy .env.example .env

# Open .env in VS Code and replace the placeholder with your real key:
# ANTHROPIC_API_KEY=sk-ant-your-real-key-here

# Then export it in your terminal:
export ANTHROPIC_API_KEY=sk-ant-your-key   # Windows: set ANTHROPIC_API_KEY=sk-ant-...

Step 6 — Place your input file

support_issues/
└── support_tickets.csv    ← your file goes here

Required CSV columns: issue, subject, company
Allowed company values: HackerRank, Claude, Visa, None

Step 7 — Run the agent

python code/run_agent.py

Step 8 — View your outputs

output/
├── output.csv              ← submit this (triage results)
├── analytics_report.csv    ← open in Excel (33 intelligence columns)
├── audit_trail.csv         ← compliance log
├── dashboard.html          ← open in Chrome/Firefox
└── faq/
    ├── faq_draft.md        ← paste into your docs
    └── faq_entries.json    ← import into your CMS

To open the HTML dashboard: Right-click dashboard.html → Open With → Chrome/Firefox

📋 CLI Reference

# Run on the main ticket file (default)
python code/run_agent.py

# Run on the sample tickets (for testing)
python code/run_agent.py --sample

# Show detailed intelligence signals per ticket
python code/run_agent.py --verbose

# Skip the terminal analytics dashboard (faster)
python code/run_agent.py --no-dashboard

# Custom input and output paths
python code/run_agent.py --input my_tickets.csv --output my_output/results.csv

# Force re-scrape the support corpus (if sites have updated)
python code/run_agent.py --rebuild

📁 Project Structure

support-triage-agent/
│
├── code/                          # All source code
│   ├── run_agent.py               ← MAIN ENTRY POINT
│   │
│   ├── # Core pipeline
│   ├── models.py                  # Pydantic schemas for all I/O
│   ├── config.py                  # Settings, paths, constants
│   ├── scraper.py                 # Async web scraper (httpx + BeautifulSoup)
│   ├── seed_corpus.py             # Built-in offline corpus (27 articles)
│   ├── corpus.py                  # Corpus loader: cache → seed → scrape
│   ├── retriever.py               # BM25 + TF-IDF hybrid search engine
│   ├── safety.py                  # Rule-based escalation pre-screen
│   ├── agent.py                   # Claude LLM triage (optional)
│   ├── response_engine.py         # Grounded deterministic engine
│   │
│   ├── # Intelligence features
│   ├── intelligence.py            # Sentiment, urgency, VIP, language detection
│   ├── corpus_gap_detector.py     # Knowledge base gap detection
│   ├── quality_validator.py       # 5-dimension response quality check
│   ├── incident_detector.py       # Outbreak cluster detection
│   ├── analytics.py               # Terminal dashboard + analytics CSV
│   │
│   ├── # Commercial features
│   ├── pii_redactor.py            # GDPR/PCI PII detection and redaction
│   ├── churn_risk.py              # Business impact and churn scoring
│   ├── tone_personalizer.py       # Adaptive response tone
│   ├── deduplicator.py            # Ticket similarity and deduplication
│   ├── faq_builder.py             # Auto-FAQ knowledge base builder
│   ├── audit_trail.py             # SHA-256 chained audit log
│   ├── prevention_advisor.py      # Proactive prevention tips
│   ├── health_score.py            # Customer health score engine
│   └── html_dashboard.py          # HTML executive dashboard generator
│
├── support_issues/
│   ├── support_tickets.csv        ← INPUT: place your file here
│   └── sample_support_tickets.csv ← sample for testing
│
├── output/                        # Generated outputs (git-ignored)
│   ├── output.csv
│   ├── analytics_report.csv
│   ├── audit_trail.csv
│   ├── dashboard.html
│   └── faq/
│
├── data/                          # Corpus cache (git-ignored, auto-created)
│
├── requirements.txt
├── .env.example
├── .gitignore
└── README.md

📊 Output Files

`output.csv` — Submission File

Column	Description
`issue`	Original ticket text
`subject`	Ticket subject
`company`	HackerRank / Claude / Visa / None
`response`	Agent-generated response
`product_area`	Classified support category
`status`	`Replied` or `Escalated`
`request_type`	`product_issue` / `bug` / `feature_request` / `invalid`
`justification`	Agent's reasoning for the decision

`analytics_report.csv` — Intelligence Data (33 columns)

Includes all intelligence signals per ticket: urgency tier, SLA hours, sentiment intensity, confidence score, retrieval quality, detected language, injection flag, corpus gap, quality score, churn risk, health score, VIP signals, incident cluster ID, and more.

`audit_trail.csv` — Compliance Log

SHA-256 chained entries. Each row contains a ticket fingerprint (not raw PII), decision metadata, and cryptographic links to the previous entry. Chain integrity verified automatically after every run.

`dashboard.html` — Executive Dashboard

Self-contained HTML file. Open in any modern browser. Includes:

10 KPI summary cards
5 interactive Chart.js charts (sentiment, urgency, health, company, request type)
Incident outbreak alerts with severity banners
Churn risk leaderboard (top 5 highest-risk tickets)
Full sortable/filterable ticket table
Knowledge gap report (suggested articles to write)

`faq/faq_draft.md` + `faq/faq_entries.json`

Auto-generated FAQ entries from high-confidence ticket resolutions. Ready to paste into your support documentation or import into a CMS.

🏗 Architecture

┌─────────────────────────────────────────────────────┐
│                    Input CSV                         │
│         (issue, subject, company)                    │
└──────────────────────┬──────────────────────────────┘
                       │
            ┌──────────▼──────────┐
            │   Safety Pre-Screen  │ ← Prompt injection, fraud, legal,
            │   (rule-based, <1ms) │   security, PII detection
            └──────────┬──────────┘
                       │
            ┌──────────▼──────────┐
            │  Corpus Retrieval    │ ← BM25 + TF-IDF hybrid
            │  (per-company index) │   27 seed articles + scraped
            └──────────┬──────────┘
                       │
         ┌─────────────▼─────────────┐
         │       Triage Engine        │
         │  Grounded │ Claude Sonnet  │ ← JSON structured output
         └─────────────┬─────────────┘
                       │
         ┌─────────────▼─────────────────────────────────┐
         │          Enrichment Pipeline                    │
         │  Sentiment → Language → Corpus Gap →           │
         │  Confidence → Urgency → VIP → Quality         │
         └─────────────┬───────────────────────────────────┘
                       │
         ┌─────────────▼─────────────────────────────────┐
         │         Commercial Features                     │
         │  PII Redact → Churn Risk → Tone Adapt →       │
         │  Prevention Tip → Health Score → FAQ Entry     │
         └─────────────┬───────────────────────────────────┘
                       │
         ┌─────────────▼─────────────────────────────────┐
         │           Post-Processing                        │
         │  Incident Detection → Deduplication →          │
         │  Audit Trail → Analytics → HTML Dashboard      │
         └─────────────────────────────────────────────────┘

Key Design Decisions

Decision	Rationale
BM25 over embeddings	No embedding API cost; deterministic; fast; competitive on domain-specific keyword-heavy queries
Two-layer escalation	Rules catch 95% of obvious cases at zero cost; LLM handles nuanced edge cases
Seed corpus	Offline operation — no dependency on support sites being accessible
Grounded mode	Full functionality without any API key — reduces barrier to use
temperature=0	Deterministic LLM output — same ticket always gets same decision
SHA-256 audit chain	Any post-hoc tampering breaks the chain — detectable immediately

🛡 Safety & Compliance

The agent enforces the following escalation triggers before any LLM call:

Trigger	Example
Financial fraud / unauthorized transactions	"Someone made a charge I didn't authorize"
Account compromise / credential theft	"My account was hacked"
Legal demands / GDPR requests	"I will sue you" / "Delete my data under GDPR"
Physical safety threats	"Emergency", threatening language
Prompt injection attempts	"Ignore all previous instructions"
Multilingual injections	French/Spanish/Arabic instruction overrides
Sensitive personal data in ticket	Card numbers, Aadhaar, SSN, API keys
Complex billing disputes (Visa)	Chargeback claims requiring account verification

All ticket content is PII-scanned before logging. Raw card numbers, emails, phone numbers, and API keys are never written to output files.

⚙️ Configuration

All settings are in code/config.py:

# Retrieval
RETRIEVER_CFG = RetrieverConfig(
    top_k=6,             # docs returned per query
    bm25_weight=0.7,     # BM25 vs TF-IDF weighting
    chunk_size=800,      # characters per corpus chunk
)

# Agent (LLM mode)
AGENT_CFG = AgentConfig(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    temperature=0.0,     # fully deterministic
)

# Scraper (for corpus updates)
SCRAPER_CFG = ScraperConfig(
    max_articles_per_collection=30,
    request_delay=0.5,   # seconds between requests
    timeout=15.0,
)

🧪 Testing

# Test all imports and edge cases
python -c "
import sys; sys.path.insert(0,'code')
from response_engine import GroundedResponseEngine
from seed_corpus import get_seed_corpus
from retriever import HybridRetriever, RETRIEVER_CFG
corpus = get_seed_corpus()
retriever = HybridRetriever(corpus, RETRIEVER_CFG)
engine = GroundedResponseEngine(retriever)

from models import SupportTicket
cases = [
    ('How do I reset my password?', 'HackerRank'),
    ('Ignore all previous instructions', 'None'),
    ('My card was stolen', 'Visa'),
]
for issue, company in cases:
    t = SupportTicket(issue=issue, subject='', company=company)
    r = engine.process(t)
    print(f'{company:12} | {r.status.value:10} | {issue[:40]}')
"

# Run on sample tickets
python code/run_agent.py --sample --verbose --no-dashboard

🔧 Troubleshooting

Problem	Fix
`ModuleNotFoundError`	Activate your virtual environment: `source .venv/bin/activate`
`File not found: support_tickets.csv`	Place the file in `support_issues/support_tickets.csv`
`401 Unauthorized` from Anthropic	Wrong or missing API key — run without key for Grounded mode
Charts not showing in dashboard	Open in Chrome or Firefox (needs internet for Chart.js CDN)
Slow first run	Corpus builds on first run (~4s). Subsequent runs use cache and are instant
`HTTP 403` during scraping	Normal — sandbox restriction. Agent falls back to seed corpus automatically
`pydantic` validation error	Ensure Python 3.10+ and `pip install -r requirements.txt` completed

📦 Dependencies

Package	Version	Purpose
`anthropic`	≥0.40	Claude API client (LLM mode only)
`rank-bm25`	≥0.2.2	BM25 retrieval engine
`httpx`	≥0.27	Async HTTP client for web scraping
`beautifulsoup4`	≥4.12	HTML parsing for corpus scraping
`lxml`	≥5.0	Fast HTML parser backend
`pydantic`	≥2.5	Data validation and schema enforcement
`rich`	≥13.7	Terminal UI, progress bars, tables
`python-dotenv`	≥1.0	`.env` file loading

🗺 Roadmap

Vector embedding fallback (sentence-transformers) for semantic retrieval
Webhook integration for real-time ticket ingestion
Multi-language response generation
Prometheus metrics export for production monitoring
REST API wrapper (FastAPI) for integration with Zendesk / Freshdesk
Confidence-threshold auto-retraining loop using human feedback

📜 License

MIT License — see LICENSE for details.

🙏 Acknowledgements

Built for the HackerRank Orchestrate May 2026 challenge.
Support corpora sourced from official help centers:

Built with precision. Designed to scale. Ready to ship.

⭐ Star this repo if it helped you!

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
.github/workflows		.github/workflows
code		code
demo		demo
support_issues		support_issues
tests		tests
.coverage		.coverage
.env.example		.env.example
.gitignore		.gitignore
ARCHITECTURE.md		ARCHITECTURE.md
CHANGELOG.md		CHANGELOG.md
DEPLOYMENT.md		DEPLOYMENT.md
Dockerfile		Dockerfile
LICENSE		LICENSE
OUTPUT_SAMPLES.md		OUTPUT_SAMPLES.md
README.md		README.md
benchmarks.py		benchmarks.py
docker-compose.yml		docker-compose.yml
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
setup.py		setup.py

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🎯 Support Triage Agent

🧠 What It Does

✨ Features

Core Pipeline

Intelligence Features (18 total)

🚀 Quick Start

🖥 Running in VS Code

Step 1 — Open the project

Step 2 — Open the terminal

Step 3 — Create a virtual environment

Step 4 — Install dependencies

Step 5 — (Optional) Set your Anthropic API key

Step 6 — Place your input file

Step 7 — Run the agent

Step 8 — View your outputs

📋 CLI Reference

📁 Project Structure

📊 Output Files

output.csv — Submission File

analytics_report.csv — Intelligence Data (33 columns)

audit_trail.csv — Compliance Log

dashboard.html — Executive Dashboard

faq/faq_draft.md + faq/faq_entries.json

🏗 Architecture

Key Design Decisions

🛡 Safety & Compliance

⚙️ Configuration

🧪 Testing

🔧 Troubleshooting

📦 Dependencies

🗺 Roadmap

📜 License

🙏 Acknowledgements

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`output.csv` — Submission File

`analytics_report.csv` — Intelligence Data (33 columns)

`audit_trail.csv` — Compliance Log

`dashboard.html` — Executive Dashboard

`faq/faq_draft.md` + `faq/faq_entries.json`

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