Reading
Introducing Email Triage OpenEnv — an open-source reinforcement learning environment for training and evaluating AI agents on tasks like customer service email classification, response, escalation, etc., featuring three levels of difficulty tasks and a refined reward mechanism.
Section 01
Introducing Email Triage OpenEnv — an open-source reinforcement learning environment for training and evaluating AI agents on tasks like customer service email classification, response, escalation, etc., featuring three levels of difficulty tasks and a refined reward mechanism.
Section 02
Current large language model (LLM) benchmarks mostly focus on 'academic' tasks such as knowledge Q&A, code generation, and mathematical reasoning. However, when it comes to deploying AI agents in real work scenarios, these tests often fail to reflect the complexity of the real world.
Customer service email handling is a typical example. This work requires:
These tasks seem simple, but they involve multi-step decision-making, context understanding, and complex state management. More importantly, the cost of mistakes is high: marking important emails as spam may lead to customer churn, while failing to identify phishing emails may pose security risks.
Section 03
Email Triage OpenEnv is an open-source reinforcement learning environment specifically designed for training and evaluating AI agents' ability to handle customer service emails. It simulates a real inbox where agents need to perform tasks like email classification, response, escalation, etc., through a series of actions.
This project is part of the OpenEnv ecosystem, which is a set of open environment standards for evaluating AI agents' performance in real-world tasks.
Section 04
At each step, the agent can observe the following information:
inbox_summary: Inbox overview containing metadata of all emails
current_email: Full content of the currently focused email
inbox_stats: Inbox statistics
task_objective: Human-readable description of the current task's goal
last_action_result: Feedback from the previous action
available_actions: List of currently available actions
Section 05
The environment defines 9 core actions:
| Action | Parameters | Description |
|---|---|---|
| focus | email_id | Read the specified email |
| classify | priority, category | Mark email priority and category |
| reply | body, tone | Send a reply (supports formal/friendly/apologetic/escalating tones) |
| escalate | escalate_to, note | Escalate to the specified team (manager/legal/technical_team/billing_team) |
| flag_spam | confidence | Mark as spam (heavy penalty for false positives) |
| archive | reason | Archive the email (resolved/irrelevant/spam) |
| mark_read | — | Mark as read |
| snooze | duration_hours | Postpone processing |
| noop | — | Do nothing (small penalty) |
Section 06
The environment provides three progressive difficulty tasks:
Section 07
The environment uses refined reward shaping, providing continuous feedback signals during the task instead of only giving a total score at the end.
Section 08
| Event | Reward |
|---|---|
| Correct priority classification | +0.10 |
| Correct category classification | +0.10 |
| Correct reply tone | +0.08 |
| Non-empty reply content | +0.04 |
| Correct escalation team | +0.12 |
| True positive spam marking | +0.10 |
| Correct archiving (spam/resolved) | +0.05 |
| Read email (focus action) | +0.01 |
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