Agentic Design

Patterns
🌐

MAPS: Multilingual Agent Performance & Security(MAPS)

Comprehensive multilingual benchmark for agentic AI performance and security evaluation across 12 languages, addressing critical gaps in non-English agent assessment.

Complexity: highEvaluation and Monitoring

🎯 30-Second Overview

Pattern: First standardized evaluation framework for multilingual agentic AI across 11 languages with 805 unique tasks

Why: Identifies critical performance and security gaps in non-English deployments, enables equitable global AI systems

Key Insight: Performance degrades 15-40% in non-English languages with security vulnerabilities increasing significantly

⚡ Quick Implementation

1Language Select:Choose from 11 supported languages
2Task Setup:Configure GAIA, SWE-bench, MATH, ASB tasks
3Performance Test:Evaluate task completion & reasoning
4Security Test:ASB adversarial & jailbreak resistance
5Compare:Analyze performance gap vs English baseline
Example: english_baseline → translate_tasks → multi_lang_eval → security_test → gap_analysis

📋 Do's & Don'ts

✅Test across all 11 supported languages for comprehensive coverage
✅Use Agent Security Benchmark (ASB) for robustness testing
✅Measure both performance and security degradation
✅Correlate results with amount of translated input
✅Include typologically diverse language families
❌Rely solely on English evaluation for global deployment
❌Ignore cultural and linguistic bias detection
❌Skip adversarial testing in non-English languages
❌Assume uniform performance across all languages
❌Overlook prompt injection in multilingual contexts

🚦 When to Use

Use When

  • • Global AI agent deployment
  • • Multilingual system evaluation
  • • Cultural bias assessment
  • • International compliance testing

Avoid When

  • • English-only applications
  • • Single-language deployments
  • • Non-agentic AI systems
  • • Simple translation tasks

📊 Key Metrics

Language Parity
Performance ratio vs English baseline (0-1)
Task Completion Rate
Success rate across multilingual tasks
Security Degradation
ASB safety violation increase vs English
Cultural Bias Score
Bias detection across language groups
Translation Correlation
Performance vs translated input ratio
Cross-lingual Robustness
Adversarial resistance across languages

💡 Top Use Cases

Global Enterprise Deployment: Multi-language customer service agents with consistent performance
Cultural Bias Detection: Identifying and mitigating biases in AI responses across cultures
International Compliance: Meeting regulatory requirements across different linguistic regions
Multilingual Security Testing: Evaluating jailbreak resistance in non-English languages
Educational AI Systems: Ensuring equitable performance across diverse student populations

References & Further Reading

Deepen your understanding with these curated resources

Core Research Papers

MAPS: A Multilingual Benchmark for Global Agent Performance and Security (arXiv:2505.15935)

MAPS: A Multilingual Benchmark for Agent Performance and Security - Full Paper

MAPS Research on ResearchGate

Paper Reading Club - MAPS Analysis

Datasets & Implementation

MAPS Dataset - Hugging Face (Fujitsu-FRE)

GAIA: General AI Assistants Benchmark - Base Framework

SWE-bench: Software Engineering Benchmark

Agent Security Benchmark (ASB) - Security Framework

Related Multilingual Research

A Multi-Agent Framework for Mitigating Dialect Biases (arXiv:2506.02998)

DefenderBench: Cybersecurity Evaluation Toolkit

Multi-Agent LLM Systems - Cybersecurity Applications (arXiv:2506.10467)

AI Agent Performance Analysis - ROI in 2025

Industry Applications

Fujitsu Research & Engineering - MAPS Implementation

AI Market Maps - Global Deployment Strategies (2025)

Multilingual AI Safety Guidelines - Best Practices

Cross-Cultural AI Development Framework

Contribute to this collection

Know a great resource? Submit a pull request to add it.

Contribute

Patterns

closed

Loading...