Building and evaluating AI Agents — Sayash Kapoor, AI Snake Oil
AI Summary
Summary of Video ‘AI Agents at Work’
- Introduction to AI Agents
- Theme: The potential and current shortcomings of AI agents in real-world applications.
- Current state: Growing interest from product developers, the industry, and academic research.
- Definitions and Examples
- AI agents are seen as components of larger systems rather than standalone products.
- Notable examples include:
- OpenAI’s operator for open-ended tasks
- Deep research tools for report writing.
- Challenges in AI Agent Development
- Evaluation Difficulty: Accurate evaluation of agents is complex.
- Example: ‘Do Not Pay’ startup faced FTC fines for failure to deliver on performance claims.
- Legal tech firms like LexisNexis have also faced issues with hallucinations in AI-generated reports.
- Misleading Static Benchmarks: Traditional evaluation methods do not capture the dynamic nature of agent interactions.
- Importance of cost considerations in agent evaluations given varying operational scalability.
- Developing Reliable AI Agents
- The debate between capability vs. reliability:
- Capability: What a model can do.
- Reliability: Consistent performance over time.
- Need for a mindset shift toward reliability engineering in AI development.
- Conclusion: Emphasis on developing robust evaluations and enhancing reliability to ensure AI agents are effective and beneficial in real-world applications. Takeaway: AI Engineers must prioritize system reliability to avoid product failures.