To get high-quality, autonomous work from an AI agent, you must treat it like a new hire, not just give it a simple prompt. You must provide a clear goal, specific skills (pre-defined knowledge), the right tools (APIs, etc.), and rich context (company data).

Unlike simple chat models that provide answers to questions, AI agents are designed to autonomously achieve a goal. They operate in a continuous 'observe, think, act' loop to plan and execute tasks until a result is delivered, moving beyond the back-and-forth nature of chat.

The '/Goal' primitive in AI assistants like Codex is not a bigger prompt but a fundamentally different interaction. It defines a desired end state and success criteria, allowing the AI to loop, self-evaluate, and work autonomously until the 'contract' is fulfilled. This moves beyond the standard back-and-forth chat paradigm.

Evolve your interaction with AI from a manual, iterative prompting process to one of system design. The advanced approach is to architect 'agent loops' where you set a high-level goal and clear evaluation criteria, then allow the AI to iterate on its own. This reframes your role from active manager to systems architect.

The most sophisticated AI users are no longer just prompting. They are creating automated "loops" where software prompts AI agents, evaluates the output, and re-prompts them to achieve complex goals with minimal human intervention. This shift from conversational partner to systems architect marks the next evolution in knowledge work.

Elias Torres argues that the current AI paradigm, which focuses on tools that assist humans (e.g., summarizers, drafters), is fundamentally limited. He believes true value is unlocked when you can instruct an AI to perform a task *infinitely* on its own, without requiring a human to type into a chat box for every action.

Current Generative AI acts as a passive co-pilot, responding to prompts for single tasks. The emerging 'Agentic AI' is an active autopilot, capable of planning and executing multi-step workflows across different tools, fundamentally changing how complex work is accomplished.

Agent loops are a new method where a user provides a high-level goal (e.g., 'create my monthly budget') instead of discrete instructions. The AI then autonomously plans, executes, and iterates in a loop until the objective is met, requiring far less manual human intervention and prompt engineering.

XAI's adoption of a "Goals Primitive," following OpenAI, signals a fundamental shift in AI interaction. Instead of step-by-step prompting, users define a high-level outcome, and the AI autonomously orchestrates sub-agents to achieve it. This is a new, foundational UX element for AI.