# Meta AI Hackathon Customer Service Simulator: An Agent Evaluation Environment Based on Deterministic Scoring

> A realistic agent environment designed for the Meta AI Hackathon, which evaluates AI agents' ability to handle multi-turn conversations by simulating complex customer service scenarios and a deterministic scoring system.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-07T15:45:45.000Z
- 最近活动: 2026-04-07T15:55:31.704Z
- 热度: 159.8
- 关键词: AI Agent, 客服模拟, Meta AI, Hackathon, OpenEnv, 确定性评分, 多轮对话, Agent评估
- 页面链接: https://www.zingnex.cn/en/forum/thread/meta-ai-hackathon-agent
- Canonical: https://www.zingnex.cn/forum/thread/meta-ai-hackathon-agent
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: Meta AI Hackathon Customer Service Simulator: An Agent Evaluation Environment Based on Deterministic Scoring

A realistic agent environment designed for the Meta AI Hackathon, which evaluates AI agents' ability to handle multi-turn conversations by simulating complex customer service scenarios and a deterministic scoring system.

## Project Overview

MetaAIHackathon is a real-world agent environment specifically designed for the Meta AI Hackathon, aiming to evaluate AI agents' ability to handle complex customer service scenarios. Unlike traditional manual evaluation, this project uses a **deterministic scoring system** to provide an objective and repeatable measure of agent performance.

## Real Scenario Simulation

The project places AI agents in real customer service support roles, requiring them to solve multi-turn customer problems. This design breaks through the limitations of traditional benchmark tests—it is not just simple question-answer pairs, but a complete dialogue process that requires understanding context, showing empathy, and advancing problem-solving through multi-turn interactions.

## Deterministic Scoring Mechanism

The scoring system evaluates agent performance from two dimensions:
- **Professionalism**: Language style, politeness level, and standardization of response structure
- **Task Completion**: Whether the problem is accurately understood, effective solutions are provided, and escalation is done correctly when necessary

The score ranges from 0.0 to 1.0 as a floating-point number, ensuring results are comparable and traceable.

## Scenario Level Design

The project designs three difficulty levels of test scenarios:

## Simple Refund Scenario (ID: 0)

Basic test scenario where the agent needs to:
- Verify the legitimacy of the refund request
- Process the refund according to standard procedures
- Confirm customer information and perform the operation

This is an entry-level test to evaluate the agent's basic ability to follow instructions.

## Medium Frustration Scenario (ID: 1)

Emotional management test where the agent needs to:
- Identify the customer's frustrated emotions
- Show a high level of empathy to soothe the customer
- Advance problem-solving only after the customer's emotions have calmed down
- Use appropriate de-escalation language

This scenario tests the agent's emotional intelligence and communication skills.

## Difficult Escalation Scenario (ID: 2)

Complex case handling where the agent needs to:
- Determine if the problem is beyond their authority
- Prepare a complete case background summary
- Execute the formal manager handover process
- Ensure the customer understands the reason for escalation and the expected timeline

This is the ultimate test of the agent's judgment and ability to follow procedures.