The Gap from "Interaction" to "Collaboration"
Almost all artificial intelligence applications start from 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, has been widely imitated. From early chatbots to advanced forms with reasoning capabilities, and now to the highly anticipated Agents, none have managed to break free from this framework.
On the surface, the dialog box replicates the natural language communication habits of humans, creating the illusion of "equal dialogue with an intelligent agent" for users, thereby blurring the boundaries of AI capabilities. However, I believe this was a trick by OpenAI to initially shape the image of an "omniscient and omnipotent God," and also a technical compromise:
The system needs a clear container for instructions, and humans need a familiar interaction anchor to alleviate the unfamiliarity with new technology.
Yet, this compromise has also set invisible shackles for AI development—it confines AI to the role of a "question-answering machine" rather than empowering it with the identity of an "actor." Users input instructions, 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. This runs counter to the autonomous action philosophy advocated by "Agentic Agents."
The Limitations of Traditional Click-Based Interaction
The vast majority of "AI products" today remain bound by "responsive interaction": users initiate instructions, and the system outputs results.
This interaction model relies on three implicit premises:
- Users clearly know their needs;
- Users understand the functional boundaries of the system;
- The system does not need to understand the environment it is in.
During the mobile internet era, these premises still held—tools only needed to passively execute commands without actively perceiving user intent. Even with the later addition of user profiling and recommendation algorithms, the core logic merely shifted from "completely passive" to "inducing users to generate intent," without achieving a fundamental breakthrough.
However, in real-world scenarios, the interplay between people, information, and the environment is often complex and ambiguous. The flaws of "dialog" as a responsive interaction method become apparent:
- It can only passively wait for user input and cannot proactively identify problems;
- It overly relies on users to set goals, placing a significant 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 with unclear goals or incomplete conditions;
- The data cluttered on the screen is disconnected from the actual need to solve problems.
The "dialog box" is the product of users turning themselves into algorithm interpreters. Users must first translate their needs into "machine-understandable language" to receive services. 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 key points; document editing, leveraging its natural language processing capabilities to complete repetitive tasks like writing; and emotional companionship, relying on its human-like responses to provide emotional support.
These three scenarios are popular (or are the only major scenarios for AI) precisely because of the constraints of "Q&A-style interaction"—to match the interaction model, users can only use AI in these effortless scenarios.
But for this very reason, AI's cross-language information processing capabilities remain locked within limited scenarios, failing to unleash its true potential.
The Core of Agentic Agents Lies in Intent Recognition
The core connotation of "Agentic" is an individual with autonomous intent and execution capabilities. 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 that an agent should not be a passive "interface" but an organizational form with autonomous responsiveness and goal-oriented 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 are pre-set "toolboxes" where users must choose and combine tools themselves; the former are "action entities" with dynamic evolutionary logic that can autonomously allocate resources based on actual situations.
- 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 roles, users are operators in traditional apps, while they are collaborators in agents;
- In terms of information flow, traditional apps follow a one-way transmission of "user → system → result," while agents involve bidirectional interaction of "system ↔ environment ↔ user";
- In terms of decision-making mechanisms, traditional apps rely on fixed logic, while agents possess dynamic reasoning capabilities;
- In terms of learning ability, traditional apps require version updates for functional iteration, while agents can adaptively learn during task execution (large models provide services via APIs, and users are unaware of their update processes);
- In terms of organizational boundaries, traditional apps are isolated monolithic applications, while agents are network nodes that can collaborate with other agents.
Thus, the emergence of agents is not merely a change in product form but a reconstruction of the paradigm for human-computer collaboration. It allows computers to truly become "organizational participants" for the first time, rather than passive command executors.
A Wilderness Survival Case: Traditional App vs. AI Agent
To more intuitively illustrate the differences between the two, let's take the complex scenario of "wilderness survival" and compare the logic of traditional apps and agents:
| Step | Scenario Description | Traditional App Logic | AI Agent Logic | Core Difference |
|---|---|---|---|---|
| ① Perceive Environment | User falls into an unfamiliar wilderness | Pre-set "forest" scenario | Actively identifies the real environment | Passive scenario → Active perception |
| ② Check Self-State | Whether injured/hungry | Assumes state is known | Real-time detection of physiological state | Static input → Real-time detection |
| ③ Set Goals | Choose next action | User manually selects | Agent autonomously infers goals | User decision → Goal autonomy |
| ④ Search for Resources | Look for usable supplies | Fixed resource database | Dynamically discovers and generates solutions | Pre-set resources → Dynamic exploration |
| ⑤ Execute Tasks | Execution process | Fixed steps, stops on error | Flexible execution, variable paths | Fixed process → Adaptive execution |
| ⑥ Evaluate Feedback | Check if completed | Output result and terminate | Automatically evaluates and enters 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, execute decisions, and truly integrate into solutions for complex scenarios. In this context, AI is no longer an accessory part of an application but a core participant in the system ecosystem.
Why Can Agents Achieve All This? (A Qualitative Leap in Engineering Capabilities)
In the past, software relied on users to provide all context and decisions, so it could only be a "tool."
Agents can take on the role of "collaborative partners" because they already possess the following practically implemented combination of capabilities:
| Capability Upgrade Point | Practical Engineering Methods | Implemented Examples | Corresponding Capability Change |
|---|---|---|---|
| Active Environment Perception | Screenshot+OCR, DOM parsing, system event monitoring | Screen reading tools, browser agents | Doesn't wait for user to describe the problem |
| Understanding Task Structure | Schema constraints, intent parsing, task decomposition models | GPT-Action, function calling, CLI, AI IDE | Can understand "what needs to be achieved" |
| Calling Tools and Services to Execute Actions | API plugin systems, cross-application operations, automation control, function calling, MCP, Claude Agent SDK | GPTs, iOS App Actions, AutoGPT, CLI, AI IDE | Can "take action" to complete tasks |
| Verifying Execution Results and Correcting Errors | Pydantic validation, result comparison, automatic retry mechanisms | Form-filling agents, web task robots, CLI, AI IDE | Can iterate cyclically instead of terminating on error |
| Continuous Context and 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 aspect here is that various programming tools like CLI and AI IDE almost satisfy all the capabilities required for an Agentic Agent to work. However, these programming tools can rely on dialog boxes to interact with users, primarily because engineers are a relatively rational group who generally possess clear and structured thinking.
For them, dialog boxes are a highly efficient interaction method because they can clearly and completely describe their needs. Additionally, the IDE environment provides ample context for AI.
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 "defining clear boundaries and inputting explicit instructions"—users must clearly know what they want to do and what the tool can do to use it effectively. The core of scenario logic is "identifying ambiguous boundaries and proactively discovering problems"—the system can perceive complex variables in a scenario and actively provide solutions for users without requiring them to manually break down their needs.
Future AI products will no longer compete based on "what features they have" but on "whether they can handle complex scenarios." A mature agent is no longer about "providing functions" but about "integrating into scenarios"—it can proactively sense potential problems and initiate solutions even before users clearly articulate their needs.
The responsive interface interaction logic of the "dialog box" constrains AI behavior within traditional interaction paradigms, forcing agents to exist in a "Q&A" form. However, 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 single problems but a "partner" providing scenario-based solutions.
When an AI is still waiting for user input instructions, it is merely 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 interaction form of the "dialog box" is not the key issue; the real core is how we define an Agent.
If we continue to confine AI capabilities within "interface logic," believing that AI can only passively receive instructions and output results through an interface, it is like putting an engine in a horse-drawn carriage.
The future of human-computer interaction will no longer revolve around "what the user asks" but "what the agent needs to accomplish."
What agents truly need are clear goals, rich contextual information, and the space for autonomous action. When AI transitions from a "tool" to an "actor," users also shift from "operators" to "goal definers"—human-computer collaboration thus enters a new stage: 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 spaces for efficiency innovation and model innovation across various industries.