project-proposal-2025

AI Labor Marketplace

Abstract

With the development of artificial intelligence and large model technologies, an increasing amount of human jobs can be replaced by AI. When Sora generates videos in 3 minutes that used to take 3 days to produce, when Cursor accomplishes tasks in a few hours that would take expert programmers weeks using natural language commands, when ChatGPT solves academic problems through one dialogue that traditionally required traversing hundreds of articles, and when digital humans can simulate micro-expressions in real-time and perform acts that previously needed award-winning acting skills—we are witnessing not just exponential leaps in efficiency but also a shift in productivity paradigms across industries. Against this backdrop, how should humanity proceed? Should we stagnate and wait to be replaced by AI, or should we actively explore and engage in the paradigm revolution of human-machine collaboration? This might be a question worth pondering deeply.

At the current stage, although AI shines brightly in certain specific areas, there are still some deficiencies, mainly reflected in the following aspects:

• Physical world boundary restrictions: Presently, AI can help us solve complex professional issues but struggles with more mundane tasks like ordering a coffee.
• Reliability limitations: Especially in professional fields such as healthcare and law, despite AI providing seemingly good answers, these cannot be reliably acted upon without certification from professionals.
• Problem complexity limitations: AI can easily resolve task-level issues with clear instructions but finds it challenging to tackle project-level issues requiring cross-domain cooperation and truly creative challenges, such as starting a cross-border e-commerce company or building an AI labor market.

Our project aims to address these issues through eAgent and eHR to provide AI services that transcend physical boundaries and ensure reliability through human-expert collaboration. We define this approach as eAgent. It breaks the boundaries of traditional human resources, plans organizational structures based on user needs, and incorporates both human and eAgent labor in solutions to achieve true human-machine synergy.

Author

Name: Zhiyong Ma

Student number: 48989938

Functionality

• Basic Features
  • User Management: Implement user registration, login, and permission control systems based on RBAC;
  • Labor File Management: Implement functions for adding, deleting, modifying, and querying labor resume information;
  • Recruitment Demand Management: Implement functions for adding, deleting, modifying, and querying recruitment information, primarily addressing situations where existing eAgents cannot meet complex demands, necessitating the posting of recruitment tasks for human experts;
  • External Service Management: Publish services created based on eAgent externally;
  • Intelligent Search: Implement indexing and intelligent search within the site;
  • Data Analysis: Statistical analysis of site data and core metrics;
• Core Features
  • eAgent: Implement eAgent using technologies such as AI Agents, RAG, Langgraph, vector databases, etc., learning about mainstream AI application development technologies in the process;
  • eHR: Develop eHR functionality using large models and recommendation system-related technologies, decomposing user needs to generate organizational structures and recruitment needs, and matching suitable eAgents based on these needs;

Scope

The Minimum Viable Product (MVP) of the AI labor market project will focus on providing core functionalities that support the basic operations of the platform. These include:

• User Management: Basic user registration, login, and role-based access control.
• Talent Profile Management: The ability to add, delete, modify, and query workforce information for both human experts and AI agents.
• Recruitment Needs Management: Functions to post, manage, and respond to recruitment needs within the platform, facilitating the hiring of human experts when AI agents are insufficient.
• Intelligent Search: Preliminary intelligent search capabilities allowing users to efficiently find relevant information on the platform.
• eAgent & eHR Integration: Initial integration capability with eAgent, focusing on task execution with clear instructions, along with a basic eHR system for matching simple organizational structures and recruitment needs.

Quality Attributes

Quality attributes critical to the success of the AI labor marketplace include:

• Performance: Ensuring fast response times for searches and operations under high load conditions.
• Reliability: Achieving high system availability and accuracy rates of model results.
• Usability: Designing intuitive interfaces for employers and employees to simplify tasks such as posting jobs, finding jobs, and managing profiles.
• Scalability: Building a scalable architecture that allows growth in user numbers and service complexity without requiring significant rework.

Each attribute must be measurable and testable, with specific benchmarks set for performance (e.g., response time), reliability (e.g., percentage of uptime), usability (e.g., user satisfaction scores), and scalability (e.g., the ability to handle increased loads).

Evaluation

To evaluate whether the project achieves the anticipated attributes, we will implement the following strategies:

• Performance Testing: Conduct load testing to measure system performance under various conditions, ensuring predefined response time targets are met.
• Reliability Assessment: Monitor system uptime and conduct regular audits to ensure compliance with reliability standards, especially for critical services. Establish evaluation datasets for the model part to ensure model accuracy meets requirements.
• Usability Testing: Run user tests with real users, collect feedback on usability, and make necessary adjustments based on user satisfaction surveys.
• Scalability Testing: Simulate increased loads to verify the system’s ability to scale up without degrading performance or service quality.

These evaluations will provide actionable insights for product improvement, ensuring it not only meets but exceeds user expectations and operational requirements.