Shift your mindset from using AI as a tool for a specific function (e.g., a scheduler) to creating an AI agent as an employee who owns an entire outcome (e.g., 'run my marketing'). This changes the interaction from using software to delegating goals to an autonomous agent.

Dreamer's AI "Sidekick" builds apps using the same command-line interface available to human developers. This forced the team to create excellent documentation and a clear API surface, which not only enables the agent but also significantly improves the developer experience for humans, creating a virtuous cycle.

When building for AI-powered environments, design tools to be equally usable by humans and the AI model. An elegant, simple design for humans often translates directly into an effective tool for AI agents, simplifying development and promoting shared logic.

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.

AI is best understood not as a single tool, but as a flexible underlying interface. It can manifest as a chat box for some, but its real potential is in creating tailored workflows that feel native to different roles, like designers or developers, without forcing everyone into a single interaction model.

The best agentic UX isn't a generic chat overlay. Instead, identify where users struggle with complex inputs like formulas or code. Replace these friction points with a native, natural language interface that directly integrates the AI into the core product workflow, making it feel seamless and powerful.

The most effective application of AI isn't a visible chatbot feature. It's an invisible layer that intelligently removes friction from existing user workflows. Instead of creating new work for users (like prompt engineering), AI should simplify experiences, like automatically surfacing a 'pay bill' link without the user ever consciously 'using AI.'

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.

A major architectural shift is underway: instead of embedding AI features into a product, companies should treat AI as an external agent that uses the product via a CLI or API. This simplifies integration and better aligns with AI's capabilities.