AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems
AgentGrad introduces intervention-guided prompt optimization for LLM multi-agent systems, achieving state-of-the-art results with 2.5x faster optimization.
AgentGrad is a prompt optimization framework for LLM-based multi-agent systems that addresses limitations in textual gradient extraction and aggregation. It uses sequential intervention to identify the agent whose prompt modification resolves a given failure, then applies agent-level supervision and semantic gradient clustering to build generalized gradients. Experiments report state-of-the-art performance across five MAS benchmarks and a 2.5x average reduction in wall-clock optimization time versus the next-fastest baseline.
- Targets flaws in gradient extraction and aggregation of prior textual-gradient prompt optimizers
- Sequential intervention isolates the agent whose prompt change resolves each failure
- Clustering semantically similar gradients prevents mixing unrelated failure modes
- State-of-the-art across five MAS benchmarks
- 2.5x faster optimization than the next-fastest baseline
From the original article
1 min read at huggingface.co
Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the prompt design of each agent. For MAS prompt optimization, textual gradient methods that guide prompt updates using natural-language feedback have emerged as a leading paradigm.
By Jaewon Chu, Jinwoo Seo, Jaewon Cho · huggingface.co
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