The Challenge of Skill Attribution in AI Agents
As AI agents move from monolithic models to modular architectures—where agents utilize a library of specialized tools or 'skills'—determining the value of each individual skill becomes critical. Traditional valuation methods often treat skills as independent entities, failing to account for the reality that skills are frequently interdependent, hierarchical, or context-dependent. This paper argues that standard Shapley value calculations, which measure the marginal contribution of a feature to a total outcome, are insufficient for agentic workflows because they ignore the underlying structural relationships between skills.
Structure-Aware Shapley Valuation
The authors propose a 'Structure-Aware' approach to Shapley valuation. By incorporating the dependency graph of an agent's skill set, the model can more accurately distribute credit for successful task completion. Instead of assuming all permutations of skill combinations are equally likely or valid, the structure-aware framework constrains the valuation process to respect the logical prerequisites and functional hierarchies inherent in the agent's architecture. This ensures that a foundational skill (e.g., 'data retrieval') is not undervalued simply because it is a prerequisite for a more visible, high-level skill (e.g., 'data analysis').
Practical Implications for Agent Design
This valuation framework provides developers with a rigorous way to prune, optimize, and improve agent performance. By quantifying the 'worth' of each skill, builders can:
- Identify redundant or underperforming skills that contribute little to overall success.
- Optimize compute resources by prioritizing the most impactful skills during inference.
- Debug agent failures by pinpointing which specific skill in a chain of dependencies is responsible for performance degradation.
Ultimately, this research moves the field toward more interpretable and efficient agentic systems, shifting the focus from 'black-box' performance to a granular understanding of how individual components contribute to complex, multi-step reasoning tasks.