The Gap from "Interaction" to "Collaboration"
Almost all AI applications start with a seemingly universal "dialog box." Mainstream large models like ChatGPT, Claude, and Gemini all use an "input box + output box" as the user entry point.
This design, pioneered by OpenAI and widely imitated, has never escaped this framework—from early chatbots to advanced forms with reasoning capabilities, and now to the highly anticipated Agent.
On the surface, the dialog box replicates natural human language communication habits, giving users the illusion of "conversing as equals with an intelligent agent," thereby blurring the boundaries of AI capabilities. But I believe this was a trick by OpenAI to create an "omniscient and omnipotent God," as well as a technical compromise:
The system needs a clear container for instructions, and humans need a familiar interaction anchor to ease the unfamiliarity with new technology.
However, this compromise has also placed invisible shackles on AI's development—confining it to the role of a "question-answering machine" rather than granting it the identity of an "actor." Users input commands, the system outputs answers, and the information flow stops there.
AI remains in a "responsive" state, unable to actively "participate" in the closed loop of complex tasks, which runs counter to the autonomous action philosophy advocated by "Agentic Agents."
Limitations of Traditional Click Interaction
Most current "AI products" are still bound by "responsive interaction": users initiate commands, and the system outputs results.
This interaction model relies on three implicit assumptions:
- Users clearly know their needs;
- Users understand the system's functional boundaries;
- The system does not need to understand its own environment.
In the mobile internet era, these assumptions held true—tools only needed to passively execute commands without actively sensing user intent. Even with the addition of user profiles and recommendation algorithms, the core logic only shifted from "completely passive" to "inducing user intent," without a fundamental breakthrough.
But in real-world scenarios, the interplay between people, information, and the environment is often complex and ambiguous, exposing the flaws of "dialog"-based responsive interaction:
- It can only passively wait for user input, unable to proactively identify problems;
- It overly relies on users to set goals, placing a heavy cognitive and decision-making burden on them;
- Each interaction is an isolated request, making it difficult to form continuous contextual memory (OpenAI's "Memory" can partially address this);
- It is helpless when faced with "ambiguous states" where goals are unclear or conditions are incomplete;
- The data piled up on the screen is disconnected from the actual need to solve problems.
The "dialog box" is a product of users turning themselves into algorithm interpreters. Users must first translate their needs into a "machine-understandable language" to receive service. This interaction method itself greatly limits the scope of AI applications.
Currently, user scenarios for AI fall into three categories: information search, using AI to compress information and extract core points; document editing, leveraging its natural language processing for repetitive tasks like writing; and emotional companionship, relying on its human-like responses for emotional support.
These three scenarios are popular (or are the only main AI scenarios) precisely because of the constraints of "question-and-answer interaction"—to match the interaction mode, users can only use AI in these low-effort scenarios.
But precisely because of this, AI's cross-language information processing capabilities are locked into limited scenarios, failing to unleash its true potential.
The Core of Agentic Agent Lies in Intent Recognition
The core meaning of "Agentic" is an entity with autonomous intent and execution capability. For a true Agentic Agent, answering questions is just the tip of the iceberg. Its core value lies in being an "action unit" that can autonomously plan paths, allocate resources, and achieve results based on goals and environmental changes.
This means an agent should not be a passive "interface" but a goal-oriented organizational form with autonomous response capabilities—it can perceive the environment, make decisions, and mobilize resources across multiple systems to complete complex tasks.
This capability is fundamentally different from the "feature stacking" of traditional apps: the latter is a preset "toolbox" where users must choose tool combinations themselves; the former is an "action entity" with dynamic evolution logic that can autonomously allocate resources based on actual conditions.
- The difference between traditional apps and agents is a leap from static tools to dynamic systems:
- In terms of essential positioning, traditional apps are tools, while agents are action units;
- In terms of user role, users are operators in traditional apps, but collaborators in agents;
- In terms of information flow, traditional apps follow a one-way "user→system→result" path, while agents involve bidirectional "system↔environment↔user" interaction;
- In terms of decision-making, traditional apps rely on fixed logic, while agents have dynamic reasoning capabilities;
- In terms of learning ability, traditional apps require version updates for feature iteration, while agents can adaptively learn during task execution (large models provide services via APIs, so users don't perceive the update process);
- In terms of organizational boundaries, traditional apps are isolated single applications, while agents are network nodes that can collaborate with other agents.
Thus, the emergence of agents is not a simple product form replacement but a reconstruction of the human-machine collaboration paradigm. It allows computers to truly become "organizational participants" for the first time, rather than passive command executors.
Wilderness Survival Case: Traditional App vs. AI Agent
To more intuitively show the difference, let's use the complex scenario of "wilderness survival" to compare the logic of traditional apps and AI agents:
| Step | Scenario Description | Traditional App Logic | AI Agent Logic | Core Difference |
|---|---|---|---|---|
| ① Perceive Environment | User falls into unfamiliar wilderness | Preset "forest" scenario | Actively identify real environment | Passive scenario → Active perception |
| ② Check Own Status | Injured/hungry? | Assume status known | Real-time detection of physiological state | Static input → Real-time detection |
| ③ Set Goal | Choose next action | User manually selects | Agent autonomously reasons about goal | User decision → Goal autonomy |
| ④ Search Resources | Find available supplies | Fixed resource library | Dynamic discovery and solution generation | Preset resources → Dynamic exploration |
| ⑤ Execute Task | Execute process | Fixed steps, stop on error | Flexible execution, variable path | Fixed process → Adaptive execution |
| ⑥ Evaluate Feedback | Check if completed | Output result and stop | Auto-evaluate and enter next cycle | One-time completion → Continuous evolution |
Clearly, traditional apps are "usable tools," while agents are "reliable collaborative partners"—they can understand goals, assess risks, make judgments, and execute decisions, truly integrating into solutions for complex scenarios. In this context, AI is no longer an accessory to the application but a core participant in the system ecosystem.
Why Can Agents Do All This? (A Qualitative Leap in Engineering Capability)
In the past, software relied on users to provide all context and decisions, so it could only be a "tool."
The reason agents can take on the role of "collaborative partners" is that they now possess the following real-world capability combinations:
| Capability Upgrade | Actual Engineering Method | Implemented Example | Corresponding Capability Change |
|---|---|---|---|
| Active Environment Perception | Screenshot + OCR, DOM parsing, system event monitoring | Screen reading tools, browser agents | No need for users to describe problems |
| Understand Task Structure | Schema constraints, intent parsing, task decomposition models | GPT-Action, function calling, CLI, AI IDE | Can understand "what needs to be achieved" |
| Call Tools & Services for Action | API plugin system, cross-app operations, automation control, function calling, MCP, Claude Agent SDK | GPTs, iOS App Actions, AutoGPT, CLI, AI IDE | Can "do tasks" on its own |
| Verify Results & Correct Errors | Pydantic validation, result comparison, automatic retry mechanism | Form-filling agents, web task bots, CLI, AI IDE | Can iterate in a loop instead of stopping on error |
| Continuous Context & State Memory | Vector databases, time-series memory, preference learning, context engineering, Memory | Rewind, Notion AI Autopilot, Manus, CLI, AI IDE | From one-time interaction → continuous collaboration |
The most interesting part here is that various programming tools like CLI and AI IDE almost meet all the capabilities needed for an Agentic Agent to work. However, these programming tools rely on dialog boxes for user interaction because engineers are a relatively rational group with clear, structured thinking.
So for them, the dialog box is a very efficient interaction method, as they can clearly and completely describe their needs and provide sufficient context for AI through the IDE environment.
From "Tool" to "Scenario Solution"
The product form of agents marks a complete shift in AI products from "tool logic" to "scenario logic."
The core of tool logic is "define clear boundaries, input precise instructions"—users must know what they want to do and what the tool can do to use it effectively. The core of scenario logic is "identify ambiguous boundaries, proactively find problems"—the system can perceive complex variables in the scenario and proactively provide solutions without users needing to manually break down their needs.
Future AI products will no longer compete on "what features they have" but on "whether they can handle complex scenarios." A mature agent will no longer "provide functions" but "integrate into scenarios"—it can proactively perceive potential problems and initiate solutions even before users have clearly expressed their needs.
The "dialog box" as a responsive interface interaction logic confines AI behavior to traditional interaction paradigms, forcing agents to exist in a "question-and-answer" form. But true agents do not rely on language input. Ideally, an agent should be a system that can directly perceive the environment, capture key signals, infer user intent, and automatically execute tasks. It is not a tool for solving a single problem but a "partner" providing scenario-based solutions.
When an AI is still waiting for user input, it is just a more efficient "search bar." Only when it can predict context, proactively intervene, and execute tasks before the user speaks does it truly possess the attributes of an "agent."
The "dialog box" interaction form is not the key issue; the real core is how we define the Agent.
If we still confine AI's capabilities within "interface logic," believing AI can only passively receive commands and output results through an interface, it's like putting an engine in a horse-drawn carriage.
The core of future human-machine interaction will no longer be "what the user asks" but "what the agent needs to accomplish."
What an Agent truly needs is clear goals, rich contextual information, and space for autonomous action. When AI transforms from a "tool" to an "actor," users also shift from "operators" to "goal definers"—human-machine collaboration thus enters a new phase: from "command interaction" to "intent collaboration."
This transformation will not only reshape the product form of AI but also profoundly change the way humans collaborate with technology, opening up new imaginative space for efficiency innovation and model innovation across various industries.