In June 2026, a piece of news sparked discussion in the tech world—Amazon employees demanded that the Seattle city government pause the construction of new data centers. At the same time, AI voice ordering systems at North American fast-food chains had already processed millions of real orders; Microsoft's AI chief publicly stated that "calling AI something alive is dangerous"; at Apple's WWDC, Senior Vice President of Software Engineering Craig Federighi held an in-depth conversation with developers about the direction of AI and operating system integration. These seemingly scattered events point to the same coordinate:AI is comprehensively shifting from "able to converse" to "able to execute," and enterprise digital transformation is entering an unprecedented "execution layer" stage。
In the past, AI mostly stayed at the level of chatbots and content generation, but by 2026, AI Agents can already "get their hands dirty"—automatically operating backend systems, handling work orders, scheduling logistics, writing code, and generating reports. This is no longer science fiction, but an industrial reality that is happening right now.
Reviewing the evolution of AI capabilities over the past three years, one main line can be clearly seen: conversational ability → reasoning ability → execution ability.
2024 was the "year of conversation," as ChatGPT and domestic large models pushed NLP capabilities to a level close to that of humans. 2025 was the "year of reasoning," as models such as DeepSeek and the GPT-o series made breakthroughs in mathematics, programming, and logical reasoning. And in 2026, the biggest keyword in the AI industry has become "Agent"—AI no longer merely answers questions, but proactively completes tasks.
The core capabilities of AI Agents include:
This leap in capability means thatthe logic of enterprise digital transformation is being redefined. In the past, digitalization meant "using systems to complete what people do," but now AI Agent digitalization means "letting AI learn to operate systems on its own and complete multi-stage tasks."
In a report by a The Verge journalist, it was mentioned that the application of AI voice ordering in the fast-food industry is only the tip of the iceberg for AI Agent deployment. In some chain restaurants in North America, AI dialogue systems already handle more than 30% of orders during breakfast and lunch peaks, with an error rate lower than that of new human employees. Behind this is not a victory of speech recognition technology, but the maturity of a "conversation + execution" closed loop—AI can not only understand "double cheeseburger, no pickles," but also directly complete the order, send it to the kitchen display screen, and enter it into the payment system.
Similar scenarios are playing out in a large number of repetitive labor fields. Logistics, customer service, financial reconciliation, data entry, and work order dispatch—the workflows of these positions share a common feature—processes can be standardized, operations can be API-enabled, and exceptions have rule sets. And this is precisely the best soil for AI Agents to deliver value.
In the wave of AI Agents,software developmentis the industry most deeply affected and also the field that benefits most directly.
According to industry trend data, after low-code platforms are combined with AI capabilities, software development efficiency has increased by about 300% overall—this is not a vague number. Specifically, AI-assisted coding tools have evolved from "code completion" to "generating complete modules based on requirement descriptions"; AI Agents on low-code platforms can directly generate runnable front-end and back-end code from business process diagrams; in the testing stage, AI Agents can automatically generate test cases and execute regression tests iteratively.
This brings direct benefits to the fields ofWeChat developmentandMini Program development. In 2026, the WeChat mini-program ecosystem opened more AI capability interfaces—developers can call large models through WeChat Cloud's AI plugins for content understanding, intelligent recommendations, and customer service dialogue, without having to build model services themselves. For teams engaged inAPP development, AI Agents are changing the traditional "requirements communication-design-development-testing" workflow. After a product manager provides a feature description, an AI Agent can first output prototype suggestions, and then developers make fine-tuned adjustments, compressing the entire iteration cycle from the monthly level to the weekly level.
If the application of AI Agents at the software layer is already clearly visible, then inInternet of Thingsand the AIoT field, the space it opens up is even broader.
Market data shows that by 2026, the IoT + AIoT market size has already exceeded one trillion yuan. The core driving force behind this round of growth is precisely the "autonomous decision-making" capability that AI Agents give to edge devices. In the past, IoT devices were merely passive executors of "collect-upload-analyze-issue commands," but now, on-device AI Agents can complete data preprocessing, anomaly identification, and preliminary decision-making locally, reducing latency from seconds to milliseconds while also lowering cloud transmission and computing costs.
insmart community solutionsfield, this change is most intuitive. AI access control systems are no longer just "swipe a card to open the door," but instead use multimodal AI Agents to identify personnel identity, detect abnormal behavior, and coordinate with property management platforms for incident response. AI Agents on IoT management platforms can automatically schedule building lighting, air conditioning, and elevators, adjusting operating strategies in real time based on crowd density, achieving comprehensive energy savings of 15% to 25%.
Over the past two decades, enterprise digitalization has mainly evolved along the path of "ERP→CRM→OA→data middle platform," essentially moving offline business online so that data leaves traces and processes are traceable. This stage can be called "process digitalization." What is beginning in 2026 is "intelligent digitalization"—AI Agents are embedded in business flows and directly participate in execution and decision-making.
According to observations by Xiangming Technology, from the end of 2025 to mid-2026, the pace of AI Agent implementation in enterprise-level applications has accelerated noticeably, mainly in three dimensions:
This trend brings new opportunities and challenges to theShenzhen software developmentindustry. On the one hand, customers' demand for software development is upgrading from "help me build a system" to "help me deploy an AI Agent system that can handle business"; on the other hand, development teams themselves also need to master new skill stacks such as Agent orchestration, large model fine-tuning, and RAG (retrieval-augmented generation). Companies with experience inWeChat Mini Program developmentande-commerce platform developmentare adding AI Agent capabilities to standard products, providing customers with integrated solutions of "out-of-the-box + AI intelligent operations."
The comprehensive open-sourcing of domestic large models such as DeepSeek is an important catalyst for the AI Agent explosion in 2026. The direct result of open source is that enterprises no longer need to call expensive commercial model APIs for every Agent application and can deploy models with equivalent capabilities in privatized environments, which is especially critical for industries sensitive to data security (finance, healthcare, government affairs).
The contribution of open-source models is also reflected in the diversity of the Agent ecosystem. Based on models such as DeepSeek, the developer community has derived industry Agent frameworks covering scenarios such as customer service, marketing, supply chain, and production scheduling. This directly lowers the threshold forenterprise AI transformation—without needing to build a large model team, based on open-source models + industry Agent templates, small and medium-sized enterprises can also complete pilot launches of AI Agents within weeks.
In China's mobile internet ecosystem, WeChat Mini Programs are one of the largest application scenarios. In 2026, the WeChat Open Platform further released AI capabilities to developers, including large model capabilities such as intelligent customer service, content understanding, and image recognition, opened to developers in the form of cloud development plugins. This meansWeChat developmentis entering an "AI-native" stage—in the future, a Mini Program can have built-in AI Agent capabilities at launch without separately deploying AI services.
For service providers deeply engaged inMini Program development, this is an important window period. Low-threshold access to AI capabilities means the user experience of Mini Programs can rise sharply. A community group-buying Mini Program can be equipped with an AI product selection Agent (automatically analyzing best-selling categories), an intelligent customer service Agent (handling 80% of common questions), and an operations analysis Agent (automatically generating daily/weekly/monthly operations reports), and these require only a few lines of configuration code.
Returning to the Amazon news mentioned at the beginning. AI's computing power demand is growing without limit, but data center construction has been halted by urban residents—this exposes a real contradiction: the speed of AI application implementation has already exceeded the speed of infrastructure construction. For AI Agents, this is not a short-term bottleneck but a long-term constraint. It means enterprises need to evaluate AI deployment strategies more pragmatically—not all businesses are worth calling cloud-based large models, and a hybrid architecture of local Agents (on-device inference) + cloud Agents (complex tasks) will become mainstream.
This signal is especially important forInternet of Thingsand edge computing scenarios. AI Agent solutions that can truly be deployed at scale are often hybrids of "edge inference + cloud training." Insmart communitiesIn scenarios such as intelligent security and industrial quality inspection, the maturity of on-device Agents directly affects the feasibility and cost of solutions.
The AI industry in 2026 does not lack gimmicks; what it lacks is solutions that can truly be implemented. From McDonald's AI ordering to community access control AI recognition, from low-code platform AI assistance to WeChat Mini Program AI plugins, AI Agents are taking over those repetitive, standard, and high-frequency operations. This is not replacing humans, but liberating human resources from repetitive labor and investing them in more creative work.
For those currently undergoingenterprise digital transformationorganizations, the current strategy should be: find the three most repetitive things in the business, try using AI Agents, rather than waiting for a perfect solution before taking action. The pace has changed from years to months, and the mindset has changed from "what AI can do" to "who AI can execute for"—this is the true watershed of digital transformation in 2026.
Xiangming Technologyinsoftware development、WeChat development、Mini Program developmentandInternet of Thingsfields, continuously tracks the technological evolution of AI Agents, and is committed to helping enterprises find the most suitable AI implementation solutions. Welcome to visit the official website xiangmingit.com to learn more about cases and solutions.
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