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TheAgentCompany Benchmark(TAC)
Benchmarks LLM agents on consequential real-world tasks that would typically be completed by multiple job roles in a software engineering company.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: Simulated software company environment with 175 real-world professional tasks
Why: Evaluates enterprise-ready AI agents in realistic workplace scenarios with multi-tool collaboration
Key Insight: Current SOTA agents achieve ~30% completion rate, highlighting gaps in real-world deployment readiness
⚡ Quick Implementation
1Clone:git clone https://github.com/TheAgentCompany/TheAgentCompany
2Setup:Docker environment with GitLab, OwnCloud, Plane, RocketChat
3Configure:Set up agent with browser, terminal, code editor access
4Select:Choose tasks from 175 professional scenarios
5Evaluate:Run checkpoint-based evaluation with partial credit
Example: python run_evaluation.py --agent gpt-4 --tasks software_engineering --mode full
📋 Do's & Don'ts
✅Test across multiple job roles (dev, QA, PM, admin, data science)
✅Use checkpoint-based evaluation for partial credit assessment
✅Enable agent communication with simulated colleagues
✅Leverage the self-hosted environment for reproducible results
✅Focus on long-horizon, multi-step professional tasks
❌Expect high completion rates (current SOTA ~30%)
❌Skip proper Docker environment setup and configuration
❌Ignore partial credit scoring - many tasks show partial progress
❌Test only on simple automation tasks
❌Overlook agent-to-agent communication requirements
🚦 When to Use
Use When
- • Evaluating enterprise-ready agent capabilities
- • Testing work automation and digital worker scenarios
- • Assessing real-world professional task performance
- • Research on AI impact on labor markets
- • Multi-step, long-horizon task evaluation
Avoid When
- • Quick capability demos or simple task evaluation
- • Resource-constrained environments (requires full Docker stack)
- • Single-domain or narrow task assessment
- • Real-time performance testing
- • Academic-only or synthetic task evaluation
📊 Key Metrics
Full Completion Rate
% of tasks completed successfully (typically ~30%)
Partial Credit Score
Weighted score including partial progress
Task Category Performance
Success rate by job role (dev, QA, PM, etc.)
Time to Completion
Average duration for successful task completion
Communication Effectiveness
Quality of agent-to-colleague interactions
Tool Usage Proficiency
Effective use of browser, terminal, code editor
💡 Top Use Cases
Enterprise Readiness Assessment: Evaluating AI agents for real workplace deployment scenarios
Work Automation Research: Understanding which professional tasks can be automated with current AI
Digital Worker Development: Building and testing AI systems that collaborate with human colleagues
Economic Impact Analysis: Measuring AI capability progression for labor market policy research
Multi-Tool Agent Testing: Assessing agent proficiency across web browsers, terminals, and code editors
References & Further Reading
Deepen your understanding with these curated resources
Related Research
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🏢
TheAgentCompany Benchmark(TAC)
Benchmarks LLM agents on consequential real-world tasks that would typically be completed by multiple job roles in a software engineering company.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: Simulated software company environment with 175 real-world professional tasks
Why: Evaluates enterprise-ready AI agents in realistic workplace scenarios with multi-tool collaboration
Key Insight: Current SOTA agents achieve ~30% completion rate, highlighting gaps in real-world deployment readiness
⚡ Quick Implementation
1Clone:git clone https://github.com/TheAgentCompany/TheAgentCompany
2Setup:Docker environment with GitLab, OwnCloud, Plane, RocketChat
3Configure:Set up agent with browser, terminal, code editor access
4Select:Choose tasks from 175 professional scenarios
5Evaluate:Run checkpoint-based evaluation with partial credit
Example: python run_evaluation.py --agent gpt-4 --tasks software_engineering --mode full
📋 Do's & Don'ts
✅Test across multiple job roles (dev, QA, PM, admin, data science)
✅Use checkpoint-based evaluation for partial credit assessment
✅Enable agent communication with simulated colleagues
✅Leverage the self-hosted environment for reproducible results
✅Focus on long-horizon, multi-step professional tasks
❌Expect high completion rates (current SOTA ~30%)
❌Skip proper Docker environment setup and configuration
❌Ignore partial credit scoring - many tasks show partial progress
❌Test only on simple automation tasks
❌Overlook agent-to-agent communication requirements
🚦 When to Use
Use When
- • Evaluating enterprise-ready agent capabilities
- • Testing work automation and digital worker scenarios
- • Assessing real-world professional task performance
- • Research on AI impact on labor markets
- • Multi-step, long-horizon task evaluation
Avoid When
- • Quick capability demos or simple task evaluation
- • Resource-constrained environments (requires full Docker stack)
- • Single-domain or narrow task assessment
- • Real-time performance testing
- • Academic-only or synthetic task evaluation
📊 Key Metrics
Full Completion Rate
% of tasks completed successfully (typically ~30%)
Partial Credit Score
Weighted score including partial progress
Task Category Performance
Success rate by job role (dev, QA, PM, etc.)
Time to Completion
Average duration for successful task completion
Communication Effectiveness
Quality of agent-to-colleague interactions
Tool Usage Proficiency
Effective use of browser, terminal, code editor
💡 Top Use Cases
Enterprise Readiness Assessment: Evaluating AI agents for real workplace deployment scenarios
Work Automation Research: Understanding which professional tasks can be automated with current AI
Digital Worker Development: Building and testing AI systems that collaborate with human colleagues
Economic Impact Analysis: Measuring AI capability progression for labor market policy research
Multi-Tool Agent Testing: Assessing agent proficiency across web browsers, terminals, and code editors
References & Further Reading
Deepen your understanding with these curated resources
Related Research
Contribute to this collection
Know a great resource? Submit a pull request to add it.