Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 9·5 min read
Beyond "AI Helps Humans": Decision-Targeted Evaluation Design for Human-Agent Teams in the Agentic Era
TL;DR
TEAM-Design is a new evaluation framework that optimizes the decision-making process for human-agent workflows by determining which tasks should receive human-only or agent-only replays. It effectively balances the need for accurate comparisons while managing resource constraints.
✦ Why It Matters
Implement TEAM-Design to enhance decision-making in your human-agent workflows today.
Key Takeaways
- Use TEAM-Design to optimize evaluation of human-agent tasks.
- Assign replay probabilities based on task predictability and cost.
- Reanalyze existing workflows to identify potential improvements.
- Focus on tasks where one comparison is significantly harder.
How It Works
TEAM-Design assigns two probabilities to each task: one for human-only replays and another for agent-only replays. It increases the probability for the harder-to-predict comparison and decreases it when replay costs are high.
This strategic allocation ensures that the evaluation process is efficient and effective.