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krishu654/
my_env

Name: my_env
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Customer Support Ticket Triage Environment

A realistic customer support environment where AI agents learn to categorize, prioritize, and resolve support tickets - a critical real-world business function.

Environment Description & Motivation

This environment simulates a customer support ticket queue that agents must manage. Support teams worldwide handle millions of tickets daily, and efficient triage is essential for customer satisfaction and retention.

Motivation: Poor ticket management leads to customer churn, SLA violations, and lost revenue. An AI agent that can intelligently triage support tickets would help companies:

Reduce response times by 40-60%
Improve customer satisfaction scores
Prevent high-value customer churn
Optimize escalation paths

Action Space

Actions are JSON objects with the following fields:

Action Type	Required Fields	Description
`categorize`	ticket_id, category	Classify ticket (billing, technical, account, feature_request, complaint, general)
`prioritize`	ticket_id, priority	Assign priority 1-5 (1=lowest, 5=highest)
`escalate`	ticket_id, escalation_level	Escalate to higher support tier (1=team lead, 2=manager, 3=director)
`resolve`	ticket_id	Mark ticket as resolved
`request_info`	ticket_id	Request additional information from customer

Observation Space

Each observation includes:

Field	Type	Description
`tickets`	array	List of pending tickets with metadata
`queue_position`	integer	Current position in queue
`processed_count`	integer	Number of tickets processed
`task_id`	string	Task difficulty (easy/medium/hard)
`step_count`	integer	Steps taken in current episode
`sla_deadline`	integer	Hours until nearest SLA violation
`urgency_level`	string	Queue urgency (critical/normal/low)

Each ticket contains:

Customer type (premium, regular, new)
Issue type and description
Urgency indicators (keywords)
Time received and SLA remaining
Calculated urgency level

Reward Function

The reward function provides dense feedback throughout the episode:

Action	Reward	Breakdown
Correct categorization	+0.7	+0.7 for exact match, +0.3 for close
Accurate prioritization	Up to +0.6	Based on closeness to correct priority
Appropriate escalation	+0.8	+0.8 for correct level, -0.2 for unnecessary
Timely resolution	+0.5	+0.15 efficiency bonus for quick handling
Progress	+0.05 per ticket	Cumulative progress through queue
SLA compliance	-0.2 per warning	Penalty for approaching SLA violations
Completion bonus	Up to +1.0	Bonus based on SLA performance
Invalid actions	-0.3	Penalty for targeting wrong ticket

Tasks

Easy Task: Basic Ticket Categorization

Tickets: 4 clear-cut tickets
Objective: Categorize each ticket correctly
Success Criteria: 80%+ categorization accuracy, 0 SLA violations
Expected Baseline: 0.85-0.90

Medium Task: Priority Management with SLA Constraints

Tickets: 5 tickets with varying urgency
Objective: Balance premium vs regular customers, respect SLA deadlines
Success Criteria: 70%+ priority accuracy, <2 SLA warnings
Expected Baseline: 0.65-0.75

Hard Task: Complex Escalation and Retention

Tickets: 6 complex tickets including churn risks
Objective: Identify retention risks, escalate appropriately, optimize satisfaction
Success Criteria: 85%+ strategic decisions, proper escalation levels
Expected Baseline: 0.50-0.60

Setup & Usage

Local Development

bash

# Clone repository
git clone <repository-url>
cd customer-support-env

# Install dependencies
pip install -r requirements.txt

# Set OpenAI API key (for baseline agent)
export OPENAI_API_KEY="your-api-key-here"

# Run baseline evaluation
python baseline/baseline_agent.py

# Run tests
pytest tests/test_env.py -v

krishu654/my_env