Why AI Agents Will Become the Dominant Form of AI-Native Applications

From Tool Calling to Task Delegation

Before large language models, AI usage followed a "plugin logic": humans decided what to do, selected tools, and AI provided specific functions like recognition, generation, or recommendation. The complete workflow still required human operation, combination, and judgment. This was essentially "human controls process, AI assists execution."

Now, large models possess more complex capabilities—understanding intent, decomposing goals, maintaining context, and executing processes. AI can not only understand tasks but also decide how to execute them and how far to go. This marks a leap from "tool logic" to "intelligent logic." In this transition, agents naturally carry the entire task execution process; they are not merely model wrappers but the form in which AI truly "moves."

Three Key Elements of AI-Native Applications

If native applications are defined as "systems built for AI from the ground up with independent operational capability," agents exhibit three core characteristics:

Goal-driven : Agents are not passive responders; they orient toward goals and autonomously plan paths.

Autonomy : They can decompose tasks, invoke tools, handle state changes, forming closed-loop processes rather than single-step responses.

Memory-aware : Agents remember user intent, historical operations, and external data, enabling dynamic adjustment and continuous service.

Therefore, agents are not "upgraded chat tools" but the fundamental form of "AI system executors."

From "Callable" to "Delegatable"

This distinction marks the biggest difference between native applications and plugin-style applications:

Driving mode : Plugin tools require human initiation and manual combination; native agent systems accept human delegation and execute autonomously.

System positioning : Plugins are assistive capabilities; agents are executors.

Behavior pattern : Plugins wait for calls; agents proactively respond and orchestrate processes.

User burden : Plugins require users to know how to use them; agents only need users to state what they want done.

For example:

Plugin era: I click "speech-to-text" → get text → manually copy to document → send to colleague.

Agent era: I say "summarize the last meeting into a memo and send it to the group," and it recognizes → generates → formats → sends, completing the loop.

This is the true meaning of "I say one sentence, it does a chain of actions"—not "it answers one question."

APP Logic Yielding to Agent Logic

The APP world is "human clicks functions"; the Agent world is "human expresses intent."

Driving force : APPs are function-driven; agents are goal-driven.

Interaction : APPs are UI-led; agents are semantics-led.

Process : APPs have fixed processes; agents have adaptive processes.

Multitasking : APPs require switching between multiple software; a single agent handles multiple tasks.

Agents restructure not only the interaction interface but also task organization , system architecture , and user usage patterns . This is not an "upgraded APP" but a reshaping of next-generation applications.