Does The Way You Plan Matter? An Empirical Study of Planning Representations for LLM Web Agents

Researchers introduce PlanAhead, a framework that systematically evaluates how different natural language plan representations affect LLM-based web agent performance across multiple AI models. The study finds that both the plan formulation method and underlying LLM significantly impact agent robustness, with implications for improving autonomous AI systems that interact with web interfaces.

The research addresses a fundamental challenge in autonomous AI systems: how planning representation affects task execution quality. LLM-based web agents currently struggle with incomplete exploration, missed critical steps, and constraint sensitivity—issues the authors attribute to inadequate planning mechanisms. By introducing PlanAhead, they bridge a gap in the literature by empirically testing whether different natural language plan formats meaningfully impact agent success rates.

The study's methodology demonstrates rigor in experimental design. Rather than relying on subjective difficulty assessments, the team automatically categorized tasks into three difficulty levels, focusing evaluation on hard tasks to reveal meaningful differences between approaches. Testing four distinct plan representations—sequential subgoals, narrative, pseudocode, and checklist formats—across multiple LLM families (OpenAI, Alibaba, Google) provides cross-platform validation. The introduction of novel metrics like Achievement Rate and Solved-Task Consistency accounts for the inherent variability in LLM outputs, addressing a common weakness in AI evaluation studies.

The findings carry significant implications for the AI industry. As enterprises increasingly deploy autonomous agents for complex web-based tasks, understanding which planning representations yield more reliable performance becomes commercially valuable. The discovery that both plan formulation and the underlying model influence robustness suggests that developers cannot achieve optimal agent performance through model selection alone—planning architecture matters equally. This creates opportunities for specialized tooling and optimization techniques targeting specific plan representations.

Future work should explore whether these findings generalize across additional domains beyond web automation and investigate hybrid planning approaches that combine strengths of multiple representations.

• →Plan representation significantly influences LLM web agent performance, requiring careful design alongside model selection
• →Different LLM families (OpenAI, Alibaba, Google) show varying effectiveness with identical plan representations
• →Sequential subgoals, narrative, pseudocode, and checklist formats produce measurably different task success rates on difficult tasks
• →Novel evaluation metrics (AR and STC) account for LLM stochasticity, improving reliability of agent performance assessment
• →Planning architecture optimization represents an underexplored avenue for improving autonomous agent robustness