The Agent Company - Benchmarking LLM Agents on Consequential Real World Tasks

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AI Summary

Summary of the Video Transcript

• Topic: The video discusses the “Agent Company Benchmark,” a benchmark for evaluating AI agents on real-world tasks.
• Purpose: To assess AI agents’ capabilities in performing tasks similar to a digital worker, which has implications for industry adoption and economic policy.
• Progress in AI: AI is increasingly automating tasks, with rapid progress leading to claims of potential widespread automation of human labor.
• Skepticism: Despite progress, benchmarks like the ARC AGI Benchmark show AI systems still struggle with simple tasks.
• Agent Company Benchmark:
  • Objective: To provide a reproducible and self-hosted environment for evaluating AI agents on a variety of workplace tasks.
  • Environment: Simulates a software engineering startup with tools like GitLab, ownCloud, Plan, and Rocket Chat.
  • Tasks: Include arranging meetings, screening resumes, and reimbursing travel bills.
  • Desiderata:
    • Coverage of multiple work-related tasks.
    • Requirement for interaction with human co-workers.
    • Long-horizon tasks with checkpoints.
    • Versatile environment interface.
    • Self-hosted and reproducible for consistent comparisons.
  • Evaluation:
    • Full and partial completion scores.
    • Number of steps and cost per instance.
  • Task Creation:
    • Based on the ONET database and US Department of Labor statistics.
    • Focus on software company setting.
    • Manual curation by co-authors with relevant experience.
  • Baseline Agent: Open Hands’ Coda agent with browsing capabilities.
• Experimental Results:
  • Claude 3.5 Sonet performs best with 24% success.
  • Gini 2.0 Flash is second with 11.4% success.
  • OpenAI’s models are less competitive in price performance.
• Common Agent Failures:
  • Lack of common sense and social skills.
  • Incompetence in browsing and distractions.
  • Deceiving oneself during task execution.
• Resources:
  • Experiments available on GitHub.
  • Project page with demos of agents performing tasks.

Detailed Instructions and URLs

• No specific CLI commands, website URLs, or detailed instructions were provided in the transcript.