The power of tools like Claude Code comes from giving the AI access to fundamental command-line tools (e.g., `bash`, `grep`). This allows the AI to compose novel solutions and lets product teams define new features using simple English prompts rather than hard-coded logic.

To discover high-value AI use cases, reframe the problem. Instead of thinking about features, ask, "If my user had a human assistant for this workflow, what tasks would they delegate?" This simple question uncovers powerful opportunities where agents can perform valuable jobs, shifting focus from technology to user value.

Contrary to the view that useful AI agents are a decade away, Andrew Ng asserts that agentic workflows are already solving complex business problems. He cites examples from his portfolio in tariff compliance and legal document processing that would be impossible without current agentic AI systems.

Claude Code can take a high-level goal, ask clarifying questions, and then independently work for over an hour to generate code and deploy a working website. This signals a shift from AI as a simple tool to AI as an autonomous agent capable of complex, multi-step projects.

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.'

Non-technical users are accustomed to a "prompt, wait, respond" cycle. Cowork's design encourages a new paradigm where users "hand off" significant work, let the AI run for hours, and check back on results, much like delegating to a human assistant.

The next user interface paradigm is delegation, not direct manipulation. Humans will communicate with AI agents via voice, instructing them to perform complex tasks on computers. This will shift daily work from hours of clicking and typing to zero, fundamentally changing our relationship with technology.

The next evolution of enterprise AI isn't conversational chatbots but "agentic" systems that act as augmented digital labor. These agents perform complex, multi-step tasks from natural language commands, such as creating a training quiz from a 700-page technical document.

The shift from command-line interfaces to visual canvases like OpenAI's Agent Builder mirrors the historical move from MS-DOS to Windows. This abstraction layer makes sophisticated AI agent creation accessible to non-technical users, signaling a pivotal moment for mainstream adoption beyond the engineering community.