The widespread adoption of AI Agent technologies is eroding the traditional traffic-entry barriers of App platforms, triggering a strategic game of offense and defense within the ecosystem. Through a combined strategy of “opening an in-platform unified super-search interface protocol + dual-layer content segmentation based on user perception and information interaction + restructuring of commercial incentive mechanisms,” a four-party balance of interests among App platforms, AI Agent service providers, content creators, and users can be achieved. This approach represents not only the most practical pathway to resolving current industry conflicts, but also a foundational paradigm for the internet ecosystem’s transition toward an era of AI-driven collaboration.
At present, the confrontation between AI Agents and traditional App platforms fundamentally stems from a dual mismatch in value propositions and technical pathways. From a technical perspective, AI Agents’ cross-application services rely on data and functional calls, yet lack standardized interfaces. As a result, they are forced to operate through non-compliant methods such as “simulating user behavior,” which in turn triggers platform security and risk-control mechanisms (e.g., abnormal account detection and data-leakage alerts), forming a cycle of technical “offense-defense attrition.” From a value perspective, App platforms anchor their core value in user attention and engagement, relying on a closed loop of “content consumption – social interaction – commercial monetization,” whereas AI Agents focus on efficiency enhancement, pursuing a minimalist path of “direct demand fulfillment – information aggregation – task execution.” This creates an inherent conflict between “experience stickiness” and “efficiency prioritization.” From a利益分配 perspective, the commercial revenues of traditional Apps and creator incentives are both tied to metrics such as exposure and interaction rates. The introduction of AI Agents diverts basic informational traffic, disrupting existing distribution mechanisms and triggering resistance from both platforms and creators. Together, these three mismatches constitute the core contradiction of the current ecosystem and force the industry to seek a breakthrough solution that accommodates multiple stakeholders.
The core strategic framework for resolving this contradiction must be built on interface openness as the foundation, content segmentation as the core, and mechanism restructuring as the safeguard. Among these, the in-platform unified super-search interface protocol serves as the key infrastructure enabling compliant collaboration between AI Agents and Apps. Its design must satisfy three industry standards. First, a tiered authorization mechanism: interface permissions are divided according to data sensitivity. Level L1 (public information, such as product specifications and news summaries) supports unrestricted AI Agent queries; Level L2 (semi-private information, such as user-saved content) requires explicit user authorization; Level L3 (sensitive information, such as payment and identity data) mandates secondary verification and full operation-log traceability to ensure data security. Second, categorized retrieval capabilities: the interface must embed a content-tagging system that allows AI Agents to precisely filter data by “user-perception content” versus “information-interaction content,” while also providing call-frequency limits and traffic-source identifiers to enable platforms to monitor service scope and user-reach paths. Third, protocol compatibility: the interface should be compatible with industry-standard Agent interaction protocols such as MCP (Model Context Protocol), unifying data formats and invocation logic to reduce cross-platform adaptation costs for AI Agent service providers and establish industry-level collaboration norms. Vipshop has already validated this model by opening standardized APIs to compliant AI price-comparison tools, enabling a closed loop of “AI invocation – traffic feedback – transaction commission.” Its interface-driven conversion rate increased by 37% compared with non-compliant channels, while user complaints declined by 62%, demonstrating the feasibility of the interface-opening approach.
On the basis of interface openness, segmenting in-platform content into user-perception content and information-interaction content, and managing them differentially in line with platform core value barriers, is the key to balancing “experience stickiness” and “efficiency enhancement.” User-perception content refers to core content assets with strong experiential attributes, high social value, and irreplaceability, including short-video creative content, social feeds, comment-section interactions, and real-time live-stream engagement. The core value of such content lies in emotional resonance and social connection, which AI Agents cannot substitute for direct user participation. Accordingly, management should follow an “experience-first” principle: only “precise content recommendation” interfaces are opened, while “content aggregation and extraction” permissions are restricted, ensuring that users must enter the App to complete immersive experiences. Key monitoring indicators include user dwell time, interaction depth (comments, shares, tipping), and revisit rates, safeguarding experiential integrity and the platform moat. In contrast, information-interaction content is defined by strong utility attributes, high information density, and standardized transmissibility, encompassing product-transaction data, educational knowledge content, government service guides, and tool-based functional interfaces. Its core value lies in efficient information delivery and task execution, which AI Agent integration can amplify. Therefore, management should follow an “efficiency-first” principle, fully opening interfaces for information retrieval, data aggregation, and task triggering, enabling AI Agents to aggregate multi-platform information for users. Practical value is measured by retrieval volume, content effectiveness scores, and user decision-conversion rates. For ambiguous boundary content (e.g., socially oriented knowledge sharing), behavioral-data-driven classification models should establish a dynamic calibration mechanism: content with social interaction accounting for over 60% is classified as user-perception content, while content with information queries exceeding 60% is classified as information-interaction content, ensuring both precision and flexibility.
Following content segmentation and interface openness, commercial monetization and creator-incentive mechanisms must be restructured in parallel to rebalance ecosystem利益. In advertising deployment, differentiated configurations are required. For user-perception content, a “native integration + low-intrusion” strategy should be adopted, with advertising budgets capped at 30%–40%, focusing on brand exposure and long-term user mindshare. Ad formats are limited to native content integrations (e.g., narrative placements in short videos or verbal recommendations in live streams) to avoid disrupting immersive experiences. For information-interaction content, a “precise matching + high conversion” strategy is appropriate, with advertising budgets raised to 60%–70%, targeting transaction conversion and service fulfillment. Ads are bound to AI Agent search results (e.g., targeted recommendations after price comparisons or service matching after knowledge queries), optimizing ROI through a full-chain data loop of “search exposure – click conversion – transaction commission.” In terms of creator incentives, a multi-dimensional evaluation system is required. For user-perception content creators, core metrics of exposure and interaction rates are retained, supplemented by content uniqueness scores and user loyalty indicators to reinforce incentives for high-quality original content and mitigate traffic-driven internal competition. For information-interaction content creators, a new evaluation framework based on “search exposure – information effectiveness – user feedback conversion” should be established. Information effectiveness is quantified via post-query completion rates (e.g., learning completion after knowledge queries or purchase rates after product information searches), while user feedback conversion is measured through satisfaction ratings and repeat-search frequency. In parallel, a dedicated long-tail content incentive fund should be established to unlock the value of niche, high-quality informational content.
This strategy not only offers strong practical value but also holds significant implications at the investment-research level. From a sector-structure perspective, its implementation will accelerate the internet ecosystem’s shift from “App traffic monopolies” toward AI Agent collaborative networks. Platforms with interface-opening capabilities and mature content-segmentation systems will gain early ecosystem dominance and become core beneficiaries of the AI era. From an opportunity-identification perspective, interface standardization will give rise to investment opportunities in AI Agent adaptation service providers and content-classification algorithm firms, while incentive-mechanism restructuring will benefit long-tail informational-creator platforms and AI-driven performance-advertising service providers. From a risk-monitoring perspective, key indicators include changes in user retention and creator income following interface openness. If in-App dwell time declines by more than 20% or creator income drops by over 15%, strategic recalibration should be initiated. Nonetheless, implementation faces three major challenges: first, the unification of industry interface standards, as variations in interface design and data formats across platforms increase adaptation costs and may slow collaboration unless industry associations or leading firms coordinate standard-setting; second, balancing data security and privacy protection, requiring full-process safeguards such as data desensitization, call traceability, and user-authorization management to comply with regulations like the Personal Information Protection Law; third, defining revenue-sharing mechanisms between platforms and AI Agents, necessitating clear attribution of AI-driven traffic and conversions and the establishment of performance-based commission models to prevent collaboration breakdowns due to利益 disputes.
In summary, this strategy represents the optimal practical pathway at the current stage for resolving the ecosystem game between AI Agents and traditional App platforms. Its core principle is “abandon confrontation and move toward collaboration.” It preserves the core value moat of App platforms while releasing the efficiency dividends of AI Agents, achieving a balanced alignment of multiple stakeholder interests. Accordingly, on the enterprise side, App platforms should prioritize pilot programs for interface openness and content segmentation in low-sensitivity sectors such as e-commerce and knowledge services, while AI Agent service providers should simultaneously advance protocol adaptation and compliance capabilities to strengthen platform collaboration. On the capital side, attention should focus on three categories of targets: leading App platforms with interface-opening and segmentation capabilities, technical service providers enabling AI Agent protocol adaptation, and vertical platforms serving long-tail informational creators. The NRCap Research Department believes that the reshaping of the internet ecosystem by AI Agents is an irreversible trend. Only by reconstructing rules around collaborative win-win principles can ecosystem value be maximized amid technological transformation, and the implementation of this strategy will mark a critical milestone in that transition.