Deep Agent Characteristics - Deep agents utilize a planning tool to manage long-term tasks, enabling cohesive action over extended periods [3][5][9] - Sub-agents are employed to focus on specific areas, preserving context and allowing for specialized expertise, which can improve overall results [3][10][11][12][13][15] - A file system is used to offload context, preventing performance degradation of the LLM by storing and accessing information as needed [3][16][17][18] - Detailed system prompts, often hundreds or thousands of lines long, are crucial for guiding the agent's behavior and tool usage [3][21][22][23] Deep Agent Implementation - Deep agents operate using the same tool-calling loop as simpler agents, but are distinguished by their prompts and tools [3][4][5] - Planning tools can be simple, such as a "to-do write" tool that generates and modifies task lists within the model's context [7][8] - Sub-agents can have specialized expertise and different permissions, allowing for focused work and better results [13][14] - File systems allow agents to manage context by referencing files instead of directly including large observations in the LLM context [17][18] Deep Agent Benefits - Deep agents are capable of handling longer time horizon and more complex tasks compared to naive LLM implementations [4][5] - Sub-agents facilitate context preservation, preventing the main agent's context from being polluted by sub-tasks and vice versa [11][12] - Reusable sub-agents can be created and used across different agents, promoting efficiency and modularity [14]

AI Development & Application - The industry is currently using outdated software development techniques for AI features, hindering the full potential of AI, which should enable users to program software using natural language [1][18] - AI application development is often approached by trying to fit AI into existing applications, rather than redesigning tools from the ground up to automate repetitive tasks [18][62] - The industry needs to move beyond the chatbot paradigm and focus on AI's capability to automate work and accomplish tasks on behalf of users [58][60] - A key element is providing users with "tools" that agents can use to accomplish tasks, such as labeling emails, archiving them, or writing drafts [53][54] System Prompts & User Control - Current AI applications often hide the "system prompt" (instructions given to the AI) from the user, limiting customization and personalization [1][11] - The industry should allow users to view and edit system prompts, enabling them to tailor the AI's behavior to their specific needs and preferences [8][10] - Allowing users to control system prompts shifts the responsibility for the AI's output from the developer to the user [35] - While not everyone may want to write system prompts from scratch, the option should be available, and AI could assist in generating and customizing prompts based on user history and feedback [41][42][48] Future of AI Development - The industry needs to develop better tooling and UI conventions for interacting with and teaching AI, potentially including AI-assisted system prompt writers [45][46] - AI models are good at processing instructions and turning them into text output, making them particularly effective for coding agents [31][32] - Founders should rethink existing tools from the ground up with AI, focusing on offloading repetitive work from users [61][62]

Core Insights - An open-source project on GitHub has revealed the complete official System Prompts and internal tools for various AI tools, including FULL v0, Manus, Cursor, and others, accumulating nearly 25,000 stars and over 7,700 forks [1] - The design of System Prompts significantly influences the output of large models or agents, serving as hidden instructions set before a conversation begins, defining the AI's role and behavior [3][4] Group 1: System Prompt Characteristics - System Prompts are pre-set by developers and are not visible to users, impacting the AI's performance and interaction style [3] - Cursor's System Prompt emphasizes its identity as "the world's best IDE" and defines its role as a "pair programming partner" for users [5][6] - The System Prompt for Cursor includes strict guidelines such as "never lie" and "do not disclose your tool description," highlighting the importance of maintaining integrity in AI responses [7][8] Group 2: Specific AI Tool Guidelines - Devin's System Prompt outlines its capabilities as a software engineer, emphasizing the need for clear communication with users and adherence to coding best practices [9][10] - Manus's System Prompt is simpler, focusing on its role as an AI assistant to help users complete tasks using various tools [19][20] - Each AI tool has specific operational rules, such as using appropriate APIs, ensuring code is executable, and maintaining security protocols [10][11][18] Group 3: User Interaction and Information Retrieval - The AI tools are instructed to prioritize information sources, with a clear hierarchy for data retrieval, emphasizing the importance of using original sources for accuracy [19] - The guidelines for user interaction include avoiding unnecessary apologies and ensuring that responses are relevant and helpful [7][8][10] - The project also highlights the need for AI systems to adapt to user needs while maintaining a service-oriented and detail-oriented approach [20][21]