Exploring Challenges in Building Effective AI Agents: Evaluation, Environment, and Reliability

Exploring the challenges and limitations of AI agents, from evaluating performance to ensuring reliability, and the need for a mindset shift in AI engineering.

1. The theme of the conference is agents at work, and the speaker will discuss the limitations of AI agents and how they can be improved.
2. There is significant interest in AI agents from various industries, including product development, industry, and research.
3. Companies are increasingly using language models as rudimentary agents for specific tasks, such as open-ended internet searches or report writing.
4. While there have been successful mainstream product offerings of AI agents, the more ambitious visions of what agents can do have not yet been realized.
5. Evaluating AI agents is difficult because productionized agents often fail, and it's essential to treat evaluation as a first-class citizen in AI engineering.
6. Static benchmarks for evaluating AI agents can be misleading, and building evaluations that account for the real-world actions of agents is harder than evaluating language models.
7. When evaluating AI agents, it's essential to consider cost alongside accuracy or performance because there isn't a fixed ceiling for the cost of open-ended actions in the real world.
8. Meaningful multi-dimensional metrics are needed to evaluate AI agents, rather than relying on a single benchmark.
9. Current static evaluations for AI agents don't provide a complete picture of an agent's capabilities and can be misleading.
10. The cost of running AI agents is still significant, especially when building applications that need to scale.
11. The Jevons Paradox suggests that as the cost of mining coal or creating language models drops, overall usage will increase, leading to higher costs.
12. A holistic agent leaderboard (Hal) has been developed to automatically run agent evaluations on various benchmarks and account for cost.
13. It's essential to have humans in the loop when evaluating AI agents to ensure that criteria are proactively edited based on domain expertise.
14. Capability and reliability are different, and it's crucial to focus on reliability when deploying agents for consequential decisions in the real world.
15. The methods for training models that get us to 90% accuracy may not be sufficient for achieving 99.999% reliability.
16. Verifiers, such as unit tests, can also be imperfect and lead to false positives.
17. AI engineering should be viewed as a reliability engineering field rather than just a software or machine learning engineering field.
18. The primary job of AI engineers is to fix the reliability issues that plague every agent that uses inherently stochastic models as its basis.
19. AI engineers need a reliability shift in their mindset, thinking of themselves as the people who ensure the next wave of computing is reliable for end-users.

Source: AI Engineer via YouTube

❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!