TheAgentCompany tests if AI can actually handle real office work, not just solve toy problems.
TheAgentCompany creates a benchmark that evaluates AI agents on real workplace tasks in a simulated software company, testing their ability to browse, code, and communicate with coworkers.
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https://arxiv.org/abs/2412.14161
🤔 Original Problem:
→ Current benchmarks lack objective ways to measure AI agents' ability to perform real workplace tasks, leading to conflicting views about AI's impact on labor automation.
→ Existing evaluations don't adequately test agents' ability to handle complex, multi-step workplace scenarios requiring both technical skills and social interaction.
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💡 Solution in this Paper:
→ TheAgentCompany simulates a software company environment with self-hosted internal websites and data.
→ The benchmark includes 175 diverse professional tasks across software engineering, project management, HR, and finance.
→ Tasks are evaluated through checkpoints that measure both full completion and partial progress.
→ Simulated colleagues powered by LLMs enable testing of workplace communication.
→ The environment uses open-source alternatives like GitLab, OwnCloud, and RocketChat for reproducibility.
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🔍 Key Insights:
→ Social interaction and complex UI navigation remain major challenges for AI agents
→ Software engineering tasks see higher success rates than seemingly simpler administrative tasks
→ Newer LLM models show improved efficiency with smaller model sizes
→ Open-source models are closing the performance gap with proprietary ones
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📊 Results:
→ Best performing model (Claude 3.5 Sonnet) achieved 24% task completion rate
→ Partial credit scoring system yielded 34.4% overall score
→ Average 29.17 steps per task with $6.34 cost
→ Tasks involving social interaction and complex UIs had lowest success rates
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