Core Insights - The article discusses the challenges and solutions in developing an enterprise-level "intelligent document analysis agent" using a hybrid architecture that combines Java, DSL encapsulated skills, and real-time rendering to ensure stability and security while retaining the flexibility of LLMs [2][28]. Group 1: Background and Challenges - The initial implementation faced challenges when users requested complex tasks, such as comparing DAU and revenue growth rates and generating Excel and PDF reports [3]. - The "pure skills" approach, which allowed LLMs to write code independently, led to significant issues in production, including arithmetic precision, file generation, and handling unstructured data [4][5]. Group 2: Architectural Evolution - The new architecture reclaims the "low-level operational rights" from LLMs, allowing them only "logical scheduling rights" [7]. - The system is divided into four logical layers: ETL layer (Java) for data flow and security, Brain layer (LLM) for intent understanding and code assembly, Skills layer (Python Sandbox) for executing calculations, and Delivery layer (Java) for rendering outputs [8][10]. Group 3: Input and Output Management - The input side now relies on Java for downloading and parsing files, ensuring that the data fed to LLMs is clean, safe, and standardized [10]. - The output strategy separates rendering and delivery, where LLMs output high-quality Markdown, which is then converted to PDF/Word by the Java backend [16]. Group 4: Skills Implementation - The implementation of DSL skills restricts LLMs from performing low-level operations directly, instead providing a set of encapsulated functions for file generation [11][14]. - A decision tree guides the LLM on when to write code and when to output text, ensuring structured and standardized outputs [14]. Group 5: Key Takeaways - The hybrid architecture retains the agent's ability to handle complex dynamic requirements while ensuring enterprise-level stability and compliance [28]. - The article emphasizes the importance of not overestimating LLMs' coding capabilities and maintaining Java's deterministic strengths in parsing, downloading, and security checks [28].