arXiv:2606.17838v1 Announce Type: new Abstract: LLM agents in interactive environments are highly sensitive to their prompts, yet prompt engineering remains a manual, task-specific process. We introduce an automated prompt optimization framework for LLM agents that decomposes the observation-to-action pipeline into a goal-conditioned descriptor agent and an action selection agent, and iteratively refines each module's prompt through an LLM-driven evolutionary loop guided by environment returns. We propose a behavior analyzer to attribute episode outcomes to specific prompt components, and a mu
The rapid advancement of large language models (LLMs) has highlighted the bottleneck of prompt engineering, making automated optimization a critical next step for practical agentic systems.
Automated prompt optimization significantly improves the reliability and efficiency of AI agents, accelerating their deployment and impact across various applications.
Prompt engineering for LLM agents can move from a manual, task-specific process to an automated, iterative refinement loop, enabling more robust and adaptable AI systems.
- · AI Agent developers
- · SaaS companies integrating LLM agents
- · Industries adopting autonomous workflows
- · Manual prompt engineers
- · Companies with inefficient AI agent deployment
Increased efficiency and capability of LLM-powered AI agents in interactive environments.
Faster development cycles and broader adoption of AI agents across diverse industries, leading to new workflow automation.
The acceleration of integrated AI systems that autonomously interact with complex digital and physical environments, reshaping enterprise software and operational paradigms.
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