The new paradigm for building powerful tools is to design them for AI models. Instead of complex GUIs, developers should create simple, well-documented command-line interfaces (CLIs). Agents can easily understand and chain these CLIs together, exponentially increasing their capabilities far more effectively than trying to navigate a human-centric UI.

While CLIs were an important stepping stone for agentic AI, the industry is rapidly moving back to rich Graphical User Interfaces (GUIs). These new UIs are designed for simultaneous collaboration between a human user and an AI agent, offering a more powerful and intuitive experience.

The evolution from terminal-based interfaces (TUIs) like early Claude Code to graphical user interfaces (GUIs) like Codex is critical. To reach a broader audience beyond developers, AI agents must offer clean, simple, and visual interfaces for managing even complex agentic workflows.

The competitive battleground for AI is shifting from raw model capability to the quality of the application layer, or 'harness.' A superior user experience, like that of OpenAI's Codex, can make a slightly weaker model more effective for daily use than a stronger model with a clunky interface. The product experience is becoming the key differentiator.

While GUIs were built for humans, the terminal is more "empathetic to the machine." Coding agents are more effective using CLIs because it provides a direct, scriptable, and universal way to interact with a system's tools, leveraging vast amounts of pre-trained shell command data.

For tools designed for AI interaction, the ease with which an agent can use the product (AX) is as critical as the user experience (UX) for humans. This can be improved by directly asking the agent for feedback on how to make the product more ergonomic for it.

An AI coding agent's performance is driven more by its "harness"—the system for prompting, tool access, and context management—than the underlying foundation model. This orchestration layer is where products create their unique value and where the most critical engineering work lies.

When deploying a complex AI agent like OpenClaw, the first step should be creating a visual dashboard. The default chat interface is a black box; a dashboard provides critical visibility into the AI's memory, skills, and scheduled jobs, making it manageable.

Designing for AI is less about crafting pixel-perfect UIs in Figma and more about creating the underlying system or "harness." This involves enabling the agent to perform long-running tasks, verify its own work, and operate effectively within technical constraints, which is where the real design work lies.