Building an internal agent: Code-driven vs. LLM-driven workflows (lethain.com)

A recent post from Imprint highlights the balance between LLM-driven and code-driven workflows in building internal agents for complex tasks. The author recounts an initial attempt to automate the review notification process using an LLM, which, while effective in theory, led to issues with inaccuracies that ultimately complicated the user experience. This experience underscored the need for a dual approach that leverages both automated language models and traditional coding practices to ensure reliability and efficiency. The implementation involves a sophisticated handler that can switch between LLM guidance and custom Python scripts based on workflow requirements. It orchestrates tool usage effectively, allowing for the execution of complex tasks by determining which method to employ—either LLM-based or code-driven—based on the context. This hybrid strategy not only enhances operational efficiency but also provides software engineers with more precise control over workflows. As Imprint continues to refine this system, it stands as a testament to the importance of selective tool utilization in AI, where the most powerful solutions are those tailored to specific needs.