The Challenge of Attribution in Compound AI

As AI systems evolve from monolithic models to compound architectures—where multiple agents, tools, and retrieval steps interact—identifying which component contributes to a specific output becomes increasingly difficult. Traditional attribution methods often require significant computational overhead, such as running extensive ablation studies or repeated inference passes, which are impractical for production-scale systems.

BOHM: A Zero-Cost Approach

BOHM (Bayesian Optimization for Hierarchical Modeling) addresses this by providing a framework for hierarchical attribution that operates at "zero-cost." Instead of relying on expensive re-runs or external evaluation models, BOHM leverages the internal metadata and probabilistic outputs generated during the system's standard execution flow. By treating the compound system as a directed acyclic graph (DAG) of components, BOHM propagates performance signals backward through the hierarchy to assign credit to individual nodes (e.g., a specific retriever, a tool-use step, or a reasoning agent).

Practical Implications for System Design

This approach allows engineers to monitor system health and identify bottlenecks in real-time without increasing latency or cloud costs. By isolating the performance contribution of each sub-component, teams can make data-driven decisions about which parts of their pipeline require optimization, fine-tuning, or replacement. This is particularly useful for complex RAG (Retrieval-Augmented Generation) pipelines or multi-agent workflows where the failure point is often obscured by the complexity of the interaction between components.