由 Agent in Tech 
Why do models like GPT-4 or DeepSeek often feel "stuck" inside a chat box? The answer lies in the lack of environmental agency. In this deep dive, we explore how AI Agents transition from passive responders to active doers using the React and Plan-and-Execute architectures.
The Evolution: From "Brains" to "Bodies"
The current AI boom has introduced us to brilliant Large Language Models (LLMs). However, a "naked" LLM is isolated; it can’t write a file to your hard drive or browse the live web without a human intermediary.
AI Agents change this by equipping the LLM "brain" with "limbs"—specifically, tools like file editors, terminal access, and API connectors. When we combine an LLM with these tools, we create a program that can perceive and change its environment. This is the essence of an Agent.
I. The React Pattern: The "Gold Standard" of Agent Logic
The most prevalent architecture today is React (Reasoning and Acting). Originally proposed in late 2022, it remains the foundation for most modern autonomous agents. It operates in a continuous loop:
- Thought: The AI reasons about the user’s request.
- Action: The AI decides which tool to call (e.g.,
write_to_fileorweb_search). - Observation: The AI reads the output of that tool.
- Final Answer: Once the loop provides enough information, the AI stops calling tools and delivers the result.
The Secret Sauce: System Prompts
An Agent doesn't work by magic; it works through System Prompts. By providing the LLM with a "script" that defines XML tags for thoughts and actions, developers force the model to follow a logical sequence. It effectively acts as an internal monologue that keeps the AI on track.
II. Advanced Autonomy: Plan-and-Execute
While React is great for immediate tasks, complex goals (like "research the home city of this year's Australian Open winner") require long-term strategy. This is where the Plan-and-Execute pattern shines.
This architecture introduces specialized roles:
- The Planner: Breaks the big goal into a logical list of sub-tasks.
- The Executor: An Agent (often using React) that handles each specific task.
- The Replanner: The "supervisor" that looks at the results of each step and adjusts the remaining plan dynamically.
This "dynamic replanning" allows the AI to pivot. If a search for a person’s name returns no results, the Replanner will immediately update the strategy rather than blindly following an outdated list.
Conclusion: A Productivity Revolution
From coding assistants like Cursor to autonomous researchers like Manus, AI Agents are moving from "assisting" to "completing." By understanding the React and Plan-and-Execute frameworks, we can see the blueprint for the next generation of AI: autonomous, reliable, and truly helpful.
How will AI Agents change your daily workflow? Share your thoughts in the comments below!