Enterprise AI Analysis

The proposed solution is an iterative, two-agent framework powered entirely by LLMs. A "Judge" LLM analyzes a sequence of actions against a high-level goal, flagging steps that are redundant, irrelevant, or missing. It provides natural language critiques. A "Planner" LLM then takes this feedback and revises the plan. This cycle repeats until the Judge finds no more errors, resulting in a clean, efficient, and logically sound action plan. This process is fully automated, language-driven, and model-agnostic, making it highly adaptable to various enterprise workflows.

The study benchmarked several state-of-the-art LLMs, revealing distinct performance trade-offs. DeepSeek-R1 acted as a conservative, high-precision judge, achieving 100% precision but lower recall (missing some errors). GPT-04-mini was more aggressive, achieving the highest recall (up to 90%) but occasionally over-correcting valid actions. Gemini 2.5 offered the most balanced performance, with consistently high F1-scores (up to 93.9%), demonstrating a sophisticated balance between identifying errors and avoiding false positives. This highlights the ability to select the right "judge" for a given enterprise risk tolerance.

While powerful, the framework currently relies solely on language and was tested on a specific household task dataset. Future enterprise applications will benefit from integrating stronger environmental grounding, such as visual object recognition or physics simulations, to enhance verification accuracy. The study also highlights the challenge of long-range dependencies in complex plans, where an action's relevance is only clear many steps later. Expanding the framework to handle these complex, multi-stage enterprise processes is a key area for future development.