AI agents are transforming how we interact with complex systems, moving software toward more autonomous architectures. My journey in this space often involves exploring how to make these agents not just intelligent, but truly effective and user-friendly. One challenge I've encountered is when an agent's output, despite being technically correct, can be verbose or unfocused, obscuring the critical information a developer needs.
This problem of verbose agent output led me to discover an interesting concept: "ADHD-friendly output". While the name references a human condition, its application here is purely about engineering agent responses for optimal clarity and actionability. It's a set of precise prompt engineering techniques and agent design patterns aimed at cutting through the noise.
In simple terms, this skill teaches an AI agent to prioritize direct, actionable information, ensuring its responses are immediately useful and easy to parse.
This approach is critical in real-world scenarios, especially for coding agents. Imagine an agent helping you debug, but its response starts with a lengthy preamble, details various possibilities, and only then, at the end, provides the actual fix. This 'burying the answer' can slow down development and reduce productivity. The "i-have-adhd" skill, as described in its open-source implementation, directly addresses this by enforcing a disciplined output structure.
Here's what the "i-have-adhd" skill emphasizes for agent responses:
- Lead with the next action: The very first line of the agent's response should be the immediate, concrete step the user needs to take. No preamble, no pleasantries, just the action.
- Number multi-step tasks: If a task requires more than one step, these steps are clearly numbered. This provides a structured, easy-to-follow guide, preventing confusion.
- End with one concrete next step: After delivering the main information or task, the agent concludes with a single, clear next step. This maintains momentum and guides the user toward progression.
- Suppress tangents: Agents are instructed to avoid discussing peripheral information or alternative approaches unless explicitly asked. The focus remains strictly on the core problem and its solution.
- Restate state every turn: In multi-turn conversations, the agent briefly reiterates the current state or context, helping the user stay oriented without needing to scroll back.
- Specific time estimates (minutes, not "a bit"): When estimating task duration, the agent provides concrete timeframes (e.g., "2 minutes") instead of vague terms, setting clear expectations.
- Make wins visible: When a sub-task or problem is resolved, the agent explicitly acknowledges the success, providing positive reinforcement and clarity on progress.
- Matter-of-fact errors: If an error occurs or a task fails, the agent reports it neutrally and factually, without apology or excessive explanation, and immediately follows with the next troubleshooting step.
- Cap lists at 5 items: To prevent overwhelming the user, lists are limited to a maximum of five items. If more are needed, the agent might suggest continuing the list on request.
- No preamble. No recap. No closers: This is a strict rule against common LLM tendencies like "Great question! Let me think about this." or "Hope this helps!" It's about direct, efficient communication.
Studying these specific prompt engineering tactics highlights how crucial precise guidance is for building effective AI agents. It's not just about what an LLM can do, but how we engineer its responses to maximize human understanding and productivity. This is a hands-on way to improve alignment and ensure AI systems are genuinely helpful. As we invest more in agentic architectures, the ability to make agents concise and actionable will be a core skill for every developer looking to not just consume AI, but contribute to its intelligent evolution.
Top comments (0)