FlowBot: Inducing LLM Workflows with Bilevel Optimization and Textual Gradients

• URL: http://arxiv.org/abs/2604.26258v2
• Date: Fri, 01 May 2026 01:42:28 GMT
• Title: FlowBot: Inducing LLM Workflows with Bilevel Optimization and Textual Gradients
• Abstract summary: Existing approaches for building powerful AI systems generally rely on human-crafted pipelines and prompts.<n>This paper describes a data-driven approach for automatically inducing agents and workflow.<n>We find that LLM discovered through our textscFlowBot induction performs competitively against strong baselines.
• Score: 33.54212868461948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM workflows, which coordinate structured calls to individual LLMs/agents to achieve a particular goal, offer a promising path towards building powerful AI systems that can tackle diverse tasks. However, existing approaches for building such workflows generally rely on human-crafted pipelines and prompts, which presents a substantial bottleneck in real world deployment. How can we automatically induce LLM-based agents and workflows in a data-driven way? This paper describes a simple data-driven approach for automatically inducing agents and LLM workflows. We formulate workflow induction as a bilevel optimization problem: an outer loop which optimizes a high-level sketch of the workflow (in particular how the LLM calls should be structured), and an inner loop which optimizes each individual LLM call one-by one. Both loops are optimized with ``textual gradients'' where for the inner loop we optimize each component in a modular way through ``backpropagating'' textual gradients layer-by-layer. We find that LLM workflows discovered through our \textsc{FlowBot} (work\textbf{flow} induction through \textbf{b}ilevel \textbf{o}ptimization and \textbf{t}extual gradients) approach performs competitively against strong baselines that make use of human-crafted or generated workflows.

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