A significant trend is quietly emerging in the artificial intelligence landscape: AI is no longer just a cloud-based tool but is becoming an "on-site" intelligent agent, with the Internet of Things (IoT) shaping AI's operational framework in the physical world. This transformation represents a fundamental shift from general intelligence to scenario intelligence, marking a new era where vertical AI models and AIoT intelligent agents are driving industrial innovation.

The convergence of AI agents and IoT creates intelligent systems capable of autonomous decision-making and real-world interaction

The Paradigm Shift: From General Intelligence to Scenario Intelligence

Over the past year, we have witnessed AI technology evolving at a dizzying pace: large language models have surged from billions to trillions of parameters, and generative AI has expanded from text to multimodal capabilities. However, this rapid advancement has revealed a critical gap between AI's impressive demonstration capabilities and its practical industrial applications.

Consider this common scenario: you ask ChatGPT to help write an email, and it produces beautifully crafted content. Yet you still need to manually copy, paste, open your email client, find contacts, and send the message. Is it "intelligent"? Certainly. Is it "practical"? Not necessarily. This illustrates the first barrier large language models encounter during industrial deployment: they can understand but cannot execute. AI can "talk" but doesn't necessarily "work."

The fundamental challenge lies in the structural differences between these two approaches. General AI models are designed for open-world scenarios with infinite possibilities, while industrial applications require closed-system solutions with highly constrained variables. Industrial AI's future belongs to vertical models because general large models face three structural obstacles: prohibitive costs, strong generalization but lack of industry expertise, and missing execution capabilities.

Scenario Intelligence: Structural Reconstruction of General Intelligence

Scenario intelligence is not a simplified version of general intelligence but represents a fundamental restructuring of AI's cognitive architecture. While general models excel in consumer applications with their ability to write poetry, create art, and engage in conversation, they encounter significant challenges when deployed in industrial settings.

Edge AI enables real-time processing and decision-making in industrial environments, from manufacturing to autonomous systems

Why do general large models experience "acclimatization issues" in industrial scenarios? The answer lies in the fundamental nature of industrial challenges. Industry is not a language arts problem; it's a physics problem. It's not an open world but a highly constrained closed system. The core focus is not content generation but controlling variables, ensuring stability, and optimizing efficiency.

This distinction becomes clear when examining performance metrics. GPT-4's activity recognition accuracy with raw sensor data is only 40%, and machine diagnostic accuracy falls below 50% – far below industrial requirements. This isn't due to insufficient parameters but inadequate "scenario understanding capability."

AI Agents vs. AIoT Agents: Understanding the Distinction

It's crucial to clarify an important but often confused concept: AI intelligent agents ≠ AIoT intelligent agents. While both are called "intelligent agents," they serve fundamentally different purposes and possess distinct capabilities.

Traditional AI agents excel at language understanding, knowledge reasoning, dialogue interaction, and content generation. They deploy in the cloud, requiring substantial computing support, but lack "hands and feet" – they cannot perceive the physical world or execute concrete actions. The core value of AI agents is "cognition."

AIoT intelligent agents, however, focus not just on cognition but on "cognition + action." They deploy at the edge or device level, connect to sensors and controllers, can perceive, decide, and execute actions, and can embed wallets for on-chain identity and settlement. AIoT agents represent complete labor units capable of seeing, thinking, working, and earning.

The Five-Stage Evolution of AIoT Intelligent Agents

True AIoT intelligent agents must possess five core capabilities to genuinely "work" from an industrial deployment perspective:

We conceptualize AIoT agent development through a "three-stage model":

Stage 1: Perception Entities can collect data and upload information. Typical forms include traditional IoT devices like cameras, sensors, and PLC controllers. The limitation: they can only "execute" but cannot "understand."

Stage 2: Collaborative Entities can understand tasks and coordinate with other devices. Built on edge AI and rule systems, typical forms include smart home systems and park automation systems. The limitation: rigid rules and lack of self-adaptation.

Stage 3: Intelligent Agents can perceive, understand, reason, act, and possess autonomous settlement capabilities. They feature LLM/SLM + planning + tool invocation + wallet + feedback systems. Typical forms include autonomous driving agents, industrial quality inspection agents, and agricultural collaboration agents.

Technical Architecture: Four Blocks + Dual Engines

The technical foundation supporting AIoT intelligent agents can be structured into four interconnected blocks, each representing a critical component of the ecosystem:

The stablecoin infrastructure enables machine-to-machine payments and autonomous economic interactions in AIoT ecosystems

The Four Technical Blocks

Chip : Represents the computational foundation of edge intelligence, including AI chips, low-power NPUs, SoCs, RISC-V architecture processors, edge AI accelerators, and heterogeneous computing units. Without "chip," there's no "operational capability" for edge intelligence.

Model : Refers to models suitable for edge deployment, especially small models and vertical models, including visual recognition models, speech recognition models, SLMs, and TinyML. Model lightweighting and specialization are key to agent deployment. Large models make AI conversational; small models make devices functional.

Terminal : The physical carrier of intelligent agents – their "body." This includes cameras, robots, industrial equipment, sensors, and edge boxes. Without these terminal devices, AI cannot connect with the real world. Terminals are the "landing points" where intelligent agents touch the world.

Intelligence (智): Represents the intelligent agents themselves, including agent platforms, scheduling frameworks, edge intelligent OS, and on-chain identity and settlement systems. It serves as the coordination center for perception, reasoning, decision-making, execution, and settlement.

Dual Engine Architecture

Two engines power this four-block system:

Engine 1: Edge Intelligence goes beyond simply deploying models to the edge; it enables intelligence to truly "station" on-site. It can respond in milliseconds, operate in offline conditions, and complete decisions locally. Edge intelligence transforms devices from "controlled objects" to "autonomous units."

Engine 2: Vertical Models address industry-specific challenges that general large models cannot solve. AIoT requires specialized models for industrial, power, security, agricultural, and other sectors. Vertical models provide intelligent agents with "professional knowledge" and "industry judgment."

Stablecoins: The "Bank Account" for Device Economy

One of the most transformative aspects of AIoT intelligent agents is their integration with financial systems through stablecoins. Stablecoins are not just Web3 tools but serve as bank accounts for AIoT devices. This integration enables a new paradigm where devices can participate directly in economic activities.

Consider these scenarios: A wind turbine supplies power to the neighboring grid – who handles settlement? A soil sensor in an orchard uploads data daily – how does it earn revenue? A laser cutting machine is "rented by the hour" – how does it automatically bill? The answer points to stablecoins.

Three Transformative Changes

Stablecoins bring three fundamental changes to AIoT ecosystems:

Economic Personality: Each device possesses a wallet address, budget, and financial boundaries, transforming from passive endpoints to economic entities.

Transaction Protocols: Device-to-device (M2M) micropayments and settlements become possible without human intervention, enabling autonomous economic interactions.

Autonomous Capability: Devices can collaborate, negotiate resources, and operate independently without platform coordination, creating truly decentralized networks.

Stablecoins enable devices to evolve from connection to collaboration, from behavior to pricing, and from hardware to economic entities. This transformation is reshaping business models from one-time "device sales" to continuous "subscription services," ultimately upgrading to "pay-per-behavior or pay-per-result" intelligent labor.

Industrial Ecosystem Organization: Platform × Protocol × Ecosystem

To drive AIoT intelligent agent industrial deployment, we must move beyond the "software as product" logic and enter the "intelligent agent as ecosystem" strategy. Intelligent agents are not "a model" but a systematic product of "platform × protocol × ecosystem," jointly built by chip/module manufacturers, platform providers, and industry partners.

The value of AIoT intelligent agent ecosystems lies not in individual strength but in collaboration. Success depends on selecting the right battlegrounds: scenarios with "high frequency, high value, and strong closed loops."

High Frequency ensures scenarios are repetitive and data-intensive, providing learning opportunities that make training "worthwhile." High Value means each action brings improvements in efficiency, cost, or quality, making execution "worthwhile." Closed Loop capability to identify problems and execute feedback ensures genuine implementation success.

Conclusion: The Future of Intelligent Systems

The transformation from general AI to scenario intelligence represents more than a technological evolution – it's a fundamental reimagining of how artificial intelligence integrates with the physical world. As we've explored, this shift involves multiple interconnected elements: vertical AI models that understand industry-specific contexts, edge computing that enables real-time decision-making, and stablecoin-powered economic systems that allow devices to participate autonomously in value creation.

The journey toward truly intelligent AIoT agents is still in its early stages, with numerous technical boundaries, application paths, and business models yet to be fully explored and validated. Whether it's more efficient model deployment to terminals, effective multi-agent system collaboration, or closed-loop machine economy operations in real environments, these challenges lack standard solutions. We stand at a point filled with possibilities.

Future exploration cannot rely on any single enterprise or technology system alone but requires collaborative effort across the entire ecosystem. The path forward demands continuous collision, validation, and refinement among industry peers, researchers, and developers to find the true landing points for AIoT intelligent agents.