SAGE: Stochastic Prompt Optimization via Agent-Guided Exploration

• URL: http://arxiv.org/abs/2606.18902v1
• Date: Wed, 17 Jun 2026 10:25:25 GMT
• Title: SAGE: Stochastic Prompt Optimization via Agent-Guided Exploration
• Abstract summary: We introduce SPO (Stochastic Prompt Optimization), a framework for search over prompt space.<n>We compare three strategies of increasing sophistication: error-informed random search, a genetic algorithm with evolutionary operators, and SAGE (SPO via Agent-Guided Exploration)<n>We argue that coupling qualitative diagnosis with quantitative validation is what makes agentic optimization effective for open-ended task-oriented dialogue.
• Score: 4.5848302154106815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context engineering has emerged as a primary lever for improving AI systems without parameter updates. Recent work showing that textual gradients do not function as real gradients motivates treating automatic prompt optimization (APO) as black-box search. We introduce SPO (Stochastic Prompt Optimization), a framework for stochastic search over prompt space, and compare three strategies of increasing sophistication: error-informed random search, a genetic algorithm with evolutionary operators, and SAGE (SPO via Agent-Guided Exploration), a multi-agent pipeline with diagnostic code execution. Across three benchmarks, no single strategy dominates; effectiveness depends on the interaction of landscape structure with error type. We further deploy SAGE on a mental-health chatbot under a continuous optimization paradigm, where it compounds eight cycles of individually-noisy A/B tests into a statistically robust gain in next-day retention. We argue that coupling qualitative diagnosis with quantitative validation is what makes agentic optimization effective for open-ended task-oriented dialogue.

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