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AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems

92 upvotes · 8 SEP 2026 · Jaewon Chu, Jinwoo Seo, Jaewon Cho et al.

This paper proposes a method to optimize prompts for multi-agent systems by identifying which agent's modification resolves a failure, and then using that agent's output as supervision to extract a fine-grained gradient. Practitioners might care because it could improve the performance of large language model-based multi-agent systems.

multi-agent systemsprompt optimizationtextual gradient methodscontinual learningbenchmarks