Prompting agents: What works and why

The blog post by Nolan Sullivan explores the intricacies of working with AI agents, contrasting them with chatbots, and emphasizing the importance of effective prompting to enhance agent performance. Unlike chatbots that operate in discrete loops, agents work continuously to achieve complex goals and can execute actions in the real world. The text delves into the layers of an agent's prompt, ranging from platform-level instructions to user requests, each playing a crucial role in the agent's behavior. The article highlights the significance of understanding system prompts, which are often lengthy and shape agent responses, and advises users on techniques to improve prompting, such as setting clear success criteria, employing the "think" tool pattern, and using XML tags for clarity in prompts. It underscores the need for specificity in instructions to prevent errors and enhance agent efficiency, advocating for the creation of persistent rule files like CLAUDE.md to maintain consistent project-wide guidelines. Additionally, it warns of potential production mishaps if prompts are not precise, encouraging users to refine prompts iteratively and leverage AI tools for feedback and improvement.