Recent discussions surrounding the notion that "software companies fall into two categories" have revealed a pivotal trend: the rise of AI agents is fundamentally reshaping the value distribution structure of the software industry. Traditional human-centric interactive software (CRM, ERP, collaboration systems, etc.) relies on per-seat subscription models, with value built upon human operations and process management. In contrast, software centered on data, algorithms, and infrastructure (databases, logging systems, monitoring, identity authentication, event streaming, etc.) operates on usage-based pricing, deriving its value from automated execution and scalable invocation capabilities.
As AI agents progressively supplant certain manual operations, seat-based SaaS faces demand contraction, while infrastructure software experiences an amplification effect due to machine invocation volumes far exceeding human click behaviors. This divergence not only impacts capital market return structures but also signals that enterprise IT architectures must migrate from "human-computer interaction dominance" to "algorithm and agent-driven" paradigms.
Against this backdrop, HaxiTAG, building upon its AI application middleware and knowledge computation framework, has introduced core innovations that include:
The fundamental innovation lies not in singular model capabilities, but in the structured governance of intelligent capabilities.
Application Scenarios and Utility Analysis
1. Human-Centric Enterprise Systems: The Fragility of Value Structures
ERP, SAP, and CRM systems are essentially containers for workflows and collaboration. Their data originates from human operations, and their decision support relies on reports and preset models. System value is highly dependent on employee headcount and usage frequency.
Following AI agent assumption of certain tasks:
- Customer service reduction → Seat reduction;
- Project management automation → Collaboration tool seat decline;
- Data entry automation → Backend system invocation decrease.
Their revenue models are tightly bound to workforce scale, presenting structural risks.
2. Algorithm-Centric Middleware Systems: Scale Amplification Effects
Infrastructure-type systems exhibit the following characteristics:
- No human-machine interface required
- Usage-based billing
- Support for automated execution
- Cross-scenario reusability
AI agent behavioral characteristics include:
- High-frequency API invocations
- Continuous database access
- Real-time event stream processing
- Comprehensive logging throughout
- Identity authentication required for each request
Machine invocation frequency far exceeds human behavior, consequently databases, logging systems, identity authentication, and risk control algorithms will experience exponential invocation growth.
HaxiTAG's AI application middleware encapsulates knowledge graphs, Know Your Transaction (KYT) algorithms, data fusion engines, and other capabilities as modular components, positioning them as "computational nodes" within AI agent execution chains, thereby:
- Enhancing reusability
- Reducing redundant development costs across scenarios
- Strengthening algorithm auditability
- Establishing a unified intelligent capability foundation
3. Trusted AI Decision Systems: Mitigating Hallucination and Drift Risks
In enterprise-grade applications, the greatest challenge of AI capabilities is not insufficient capability, but uncontrollable risk.
Based on the P–L–B computation framework:
- Semantic drift can be measured via KL divergence;
- Language compression loss can be assessed through mutual information;
- Bias-induced reasoning can be analyzed via posterior distribution separation.
This means enterprise IT can construct a "measurable semantic security layer," embedding AI decisions within:
- Data constraint layer (restricting input sources)
- Model inference layer (multi-model cross-validation)
- Result verification layer (rule engines and human threshold controls)
AI transforms from a "black-box responder" into an auditable decision agent.
Structural Insights from Industry Best Practices
1. Three-Layer Restructuring Path for Enterprise IT
Layer One: Capability Componentization
- Transform algorithmic systems into API-based capability services;
- Introduce model version management and observability;
- Establish invocation governance frameworks.
Layer Two: Agent Identity and Behavior Governance
- Establish agent identity management systems;
- Implement machine behavior quota controls;
- Strengthen invocation auditing and traceability capabilities.
Layer Three: Semantic Security and Alignment Mechanisms
- Introduce drift monitoring mechanisms;
- Establish cross-model consistency evaluation;
- Construct knowledge graphs as semantic anchors.
2. Critical Strategies for IT Enterprises to Avoid Marginalization
Traditional functional middleware (logging, storage, authentication), if not upgraded to "AI-centralized capability nodes," will be replaced by more intelligent infrastructure.
Upgrade directions include:
- Support for agent collaboration protocols;
- Event-driven interface provision;
- Support for reasoning chain recording;
- Real-time policy control provision.
The core competency of future middleware is not "whether it is available," but whether it can be embedded within the AI decision loop.
Implications and the Elevation of AI Intelligence
1. The True Core Is Not the Model, but Control
Enterprise competitiveness will depend on:
- Whether data structure sovereignty is secured;
- Whether invocation traffic governance rights are held;
- Whether semantic interpretation rights are controlled;
- Whether agent behavior auditing rights are maintained.
If enterprises merely deploy general-purpose large models without establishing capability governance frameworks, they will become "data providers subject to external invocations."
2. The Essential Leap from Digitalization to Intelligence
Enterprise IT is undergoing a triple structural transition:
Throughout this process, the "knowledge computation + AI middleware" model represented by HaxiTAG provides enterprises with a structural pathway:
- Fusing knowledge, algorithms, and data into measurable capabilities;
- Reducing hallucination risks through semantic security matrices;
- Achieving scale amplification through capability reuse;
- Building sustainable intelligent systems through agent governance.
The software industry in the AI era is not about simple replacement, but value restructuring. Seat-based SaaS and invocation-based infrastructure will accelerate their divergence. If enterprise IT continues to center on human-machine interfaces, it will progressively lose competitiveness; if it completes algorithmic capability middleware transformation and trusted intelligent architecture construction, it can occupy core nodes in the agent economy.
The core assets of future enterprises will no longer be software quantity, but rather:
- Reusable intelligent capabilities;
- Auditable decision chains;
- Controllable semantic and bias boundaries;
- Scalable agent execution systems.
The true value of AI lies not in generating text, but in reshaping the structure and power boundaries of enterprise IT.