Revolutionizing AI Agent Evaluation: A New Perspective
For the past year, I’ve been building AI agents and noticed a troubling trend: evaluations often focus solely on whether the final output is correct. But this approach overlooks critical factors.
Key Insights:
- Agents can arrive at the right answer using inefficient or incorrect methods.
- Traditional ML metrics like accuracy and precision miss intermediate hallucinations and constraint violations.
- My approach shifts the focus onto the agent’s entire trajectory, using multi-dimensional scoring to capture the full picture.
The results are transformative. I’ve been able to identify issues such as:
- Hallucinations
- Inconsistent paths
- Constraint violations
Is the industry stuck in outdated evaluation practices? I invite fellow AI enthusiasts to share their insights! How are you assessing your agents? What challenges have you faced?
Join the conversation and elevate your understanding of AI evaluation!
