arXiv:2606.04465v1 Announce Type: cross Abstract: System prompt optimization improves agent behavior without modifying the underlying model, yielding human-readable, model-agnostic instructions. Existing methods build a prompt agent that refines task agents' system prompts, yet leave the prompt agent's own system prompt hand-engineered and fixed. We propose Self-Evolving Prompt Optimization (SePO), which treats the prompt agent's own system prompt as an optimization target alongside task agents' system prompts. SePO adopts a self-referential design. A single prompt agent improves both task age
The rapid advancement of AI models and agentic systems necessitates more sophisticated and autonomous optimization techniques for system prompts, moving beyond manual tuning.
This breakthrough offers a path towards more efficient, robust, and generalizable AI agents by optimizing the underlying prompt structures that drive their behavior, reducing reliance on human intervention.
AI agent development can become more self-sufficient, as the primary prompt agent can now optimize its own system prompt alongside those of task agents, leading to recursive improvement.
- · AI Agent Developers
- · Companies deploying AI agents for complex tasks
- · Researchers in prompt engineering
- · Manual prompt engineers
- · Companies without advanced AI optimization techniques
System prompt optimization will accelerate, leading to more capable and adaptable AI agents across various domains.
The reduced need for human oversight in prompt engineering could accelerate the deployment of autonomous AI systems in new applications.
This self-evolving capability could contribute to more generalized AI systems that are better at adapting to new tasks and environments with minimal human input.
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Read at arXiv cs.AI