Multi-Agent Position Classification with Tool Orchestration: Use Case System for Occupational Taxonomy Mapping

Vahid Faraji · ACM Conference on AI and Agentic Systems (CAIS '26), San Jose, May 2026

DOI: 10.1145/3786335.3813207 · alphaxiv discussion

Summary

This paper presents a tool-augmented, MCP-based multi-agent system for job position normalisation — mapping unstructured job postings to standardised positions at scale.

• 95% accuracy on 14,000+ job positions
• 72.5% faster classification than baseline approaches
• 41.8% fewer API calls through shared caching

Architecture

The system orchestrates four specialised agents over the Model Context Protocol (MCP):

• Planner — decomposes the normalisation task
• Search — retrieves candidate standard positions
• Extraction — pulls structured attributes from postings
• Evaluation — scores and validates outputs

A shared caching layer across agents reduces redundant LLM calls while preserving accuracy.

Results

• Accuracy: 95%
• Speed: 72.5% faster than baselines
• Cost: 41.8% fewer API calls

References & Links

• ACM DL: https://dl.acm.org/doi/10.1145/3786335.3813207
• alphaxiv discussion: https://www.alphaxiv.org/abs/multi-agent-position-classification
• Author: Vahid Faraji — Applied AI Specialist