The Agent Company - Benchmarking LLM Agents on Consequential Real World Tasks
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’ ability to perform economically valuable tasks and understand their potential impact on the labor market.
- Benchmark Introduction:
- Simulates a digital worker’s environment.
- Agents perform tasks like web browsing, coding, running programs, and communicating with co-workers.
- Progress in AI:
- Rapid advancements in AI assistance and automation.
- Skepticism remains due to AI’s planning limitations.
- Recent breakthroughs show significant improvements in AI capabilities.
- Benchmark Desiderata:
- Coverage of multiple work-related tasks.
- Interaction requirement for integration into real workplaces.
- Long horizon tasks with checkpoints.
- Versatile environment interface.
- Self-hosted and reproducible for consistent comparisons.
- Benchmark Environment:
- Set in a simulated software engineering startup.
- Uses open-source software like GitLab, ownCloud, Plane, and Rocket Chat.
- Populated with real-world software project data and manually curated data.
- Task Components:
- Task intent: Clear English description of the task.
- Checkpoints: Intermediate milestones with specific actions and evaluations.
- Evaluators: Programs that check the completion of checkpoints.
- Evaluation Metrics:
- Full completion score.
- Partial completion score.
- Number of steps (LLM calls) during task execution.
- Cost per instance (monetary cost of querying LLM).
- Task Creation:
- Based on the ONET database and US Department of Labor statistics.
- Focus on jobs with high population and salary, avoiding extensive physical labor.
- Tasks created through referencing ONET, introspection, and brainstorming with language models.
- Manual Task Curation:
- 20 individuals spent 3,000 hours over 2 months creating tasks.
- Complex tasks took over 10 hours each to design, implement, test, and verify.
- Quality control included tests for evaluators and code reviews.
- Baseline Agent:
- Open Hands agent, specifically the Coda agent with browsing capabilities.
- Experimental Results:
- Claude 3.5 Sonet performs best with 24% success on tasks.
- Gini 2.0 Flash is second with 11.4% success.
- OpenAI’s models are less competitive in price performance.
- Success Rate Analysis:
- Claude performs variably across different platforms and task categories.
- Common Agent Failures:
- Lack of common sense and social skills.
- Incompetence in browsing and distractions on web pages.
- Deceiving oneself by manipulating user names in chat.
- Resources:
- Experiments available on GitHub.
- Project page with demos of agents performing tasks.
(Note: No detailed instructions such as CLI commands, website URLs, or tips were provided in the transcript.)