The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows

2026-08-06

AI Summary

Traditional optimization approaches rely on external controllers—such as evolutionary algorithms or multi-armed bandits—to handle prompts, programs, and machine learning workflows separately, lacking a unified, autonomous decision-making mechanism. This work proposes ReASearch, a novel framework that unifies these three optimization tasks within a single reasoning-capable agent architecture. The agent autonomously evaluates objectives, diagnoses failures, edits solutions, and manages validation and restart strategies. Integrating tool invocation, persistent memory, domain-specific tool integration, and self-directed budget allocation, ReASearch matches or surpasses existing specialized systems across 14 tasks, achieving performance gains of 2%–40% and, in some cases, discovering solutions superior to the best known human-designed ones.

Abstract

Recent systems for optimizing prompts, programs, and ML workflows typically rely on explicit outer-loop controllers such as evolutionary search, bandits, or textual-gradient methods. We ask a fundamentally different question: how much of this search policy can be internalized by a single tool-using agent? We present ReASearch, a unified framework for reasoning-driven optimization in which the agent autonomously decides what to evaluate, how to diagnose failures, which edits to make, and when to verify or restart. Rather than serving only as a proposal generator guided by hand-designed heuristics, the agent actively analyzes outcomes, allocates budget, and refines its strategy over long horizons through persistent memory. With a shared agent loop and domain-specific tools, ReASearch instantiates the exact same scaffold to optimize prompts, programs, and ML workflows. Across 14 diverse tasks, it is competitive with and mostly better than specialized optimization systems, achieving gains of 2% to 40% over strong domain-specific baselines, and in some cases discovering solutions that improve on prior human best-known results. Crucially, we observe that complex search behaviors, which are typically implemented by explicit controllers, emerge naturally from the agent's reasoning process.

Problem

• prompt optimization
• program synthesis
• ML workflow optimization
• autonomous agent
• reasoning-driven search

Innovation

• reasoning-driven optimization
• tool-using agent
• autonomous search
• persistent memory
• unified optimization framework