MARS: Mechanism-Aware Redesign Search for Agentic Systems

Automated agent design reduces manual engineering by searching for effective agentic systems. Representing agents as executable code enables a broad range of designs, but also creates a vast search space that is difficult to navigate. In this expansive space, coarse-grained evaluation feedback makes it difficult to attribute performance bottlenecks to specific parts of an agent’s design, resulting in a lack of targeted guidance during search. We introduce Mechanism-Aware Redesign Search (MARS), which establishes an explicit correspondence between executable agent programs and a mechanism-level semantic representation of their composition, instantiation, and interaction. This representation aligns agent design, failure attribution, and redesign at a common mechanism-level granularity. Grounded in executable code and observed execution, MARS attributes failures to individual mechanisms and their interactions, using the resulting diagnoses to guide targeted redesign. Across five tasks, MARS discovers agents that outperform the strongest automated-design baseline on each task while consuming fewer inference-time LLM tokens. Notably, MARS surpasses the best results achieved by all compared search baselines within the first two generations.