AI Multi-Agent Intelligent Governance Platform: Breaking the Deadlock of Grassroots Digital Governance

I. The "Digital Alienation" of Grassroots Governance: The More Systems, The Heavier the Burden

Over the past decade and more, various government functional departments have independently developed a huge number of business systems around their own vertical business lines. While these systems have improved the efficiency of individual business lines, they have also turned into information silos that operate independently with rigid barriers. More than 80% of the effective working hours of grassroots staff are occupied by high-frequency reporting tasks assigned by different government departments — the name of the same resident is marked as "Student Name" in the education department's report, "Name of Person in Special Difficulty" in the civil affairs department's ledger, and "Name of Key Population" in the health system. With essentially the same content, different statistical calibers and repeated reporting requirements, grassroots cadres are forced to become "data porters" and "format converters".

A bigger problem lies in the loss of knowledge. Traditional information systems only record "work order results", but never precipitate "disposal logic" — why tasks are assigned in such a way, what policies are followed, and what resources are coordinated. Governance wisdom exists implicitly in the personal experience of "old secretaries" and "old directors", and dissipates as personnel flow, resulting in repeated gaps where "people leave, experience vanishes".

Clear reform signals have been released at the policy level. In August 2024, the General Office of the Communist Party of China Central Committee and the General Office of the State Council issued the Provisions on Combating Formalism to Reduce the Burden on the Grassroots, requiring the cleanup and integration of government application programs for grassroots use, and banning "digital trace retention" through means such as taking photos, clocking in, and repeated reporting across multiple platforms. In May 2026, the National Data Administration issued the Key Points for the Work of Digital Society Development in 2026, deploying the comprehensive deepening of the construction of the "One Form Unified Submission" system to promote data-enabled grassroots governance. The Regulation on Government Data Sharing, which came into effect in August 2025, established the legal framework for government data sharing in the form of administrative regulations for the first time. The Interim Measures for the Administration of Artificial Intelligence Anthropomorphic Interactive Services, which was officially implemented on July 15, 2026, has cleared compliance obstacles for the large-scale implementation of government AI.

II. Multi-Agent Collaboration: Enabling Machines to Understand the "Semantics" of Governance and Break the Deadlock

Different from the conventional solution of adding a new set of business systems on the market, this full-link intelligent governance platform adopts a complete five-layer architecture from L1 data resource layer to L5 user layer. It does not replace the existing business systems of various government departments, but serves as an intermediate intelligent governance base to complete data aggregation, semantic analysis, task scheduling, knowledge precipitation, and multi-role service output, breaking through the collaboration barriers between existing systems. The core technical capabilities of the platform are supported by five modules together.

1. MAS (Multi-Agent System) — the "Swarm Brain" for distributed intelligent governance:

MAS efficiently completes complex governance workflows through the collaborative division of labor of multiple professional AI agents. The platform builds a parallel collaborative architecture of perception Agent, routing Agent, execution Agent and evolution Agent: the perception Agent is responsible for real-time aggregation and anomaly detection of multi-source heterogeneous data; the routing Agent realizes intelligent task assignment and merging based on semantic understanding; the execution Agent drives cross-terminal collaboration and closed-loop disposal; the evolution Agent continuously extracts knowledge and optimizes strategies from the disposal process. All Agents achieve "independent execution and seamless connection" through standardized communication protocols and task scheduling algorithms.

2. Ontology-based Governance — upgrading from "data fields" to "governance semantics": Define community governance objects (people, houses, events, objects, organizations) as computable, associable and inferable governance ontologies. Through ontology modeling, "elderly people living alone" is no longer an isolated field, but a semantic node deeply associated with multi-dimensional ontologies such as "residential address", "health record", "water, electricity and gas consumption", "emergency contact" and "community volunteer". When any associated ontology is abnormal (for example, the water meter reading remains zero for 72 consecutive hours), the system automatically triggers cross-ontology reasoning to generate an early warning event of "suspected emergency of elderly people living alone", realizing "business triggering upon data collision".

3. Semantic Routing & Adaptation — the "intelligent translator" across business lines: Based on the semantic understanding capability of large models, it automatically analyzes the instructions, report requirements and assessment indicators issued by superior departments across different lines, extracts common demands and core semantics, and realizes the automatic merging of "multi-party task assignment" into "one order with multiple responses". At the same time, it establishes a semantic mapping library of business calibers of all government departments, automatically completes the format adaptation of "the same data, multi-caliber output", and fundamentally eliminates the repeated reporting burden on the grassroots. This is the technical core of realizing "One Form Unified Submission".

4. Evolution Layer — the "evolution upon completion" mechanism based on RAG and knowledge graph:

After each work order disposal is completed, the system automatically extracts key decision nodes, policy basis, coordination paths and effect feedback in the disposal process, structures them into "semantic slices" and injects them into the ontology knowledge base. Combined with RAG (Retrieval-Augmented Generation) technology and knowledge graph, the platform can continuously accumulate governance wisdom and realize the evolution effect of "understanding the community better the more it is used". The "AI Xiaofu" digital employee system in Futian District, Shenzhen has verified this path: 43 types of government AI agents are fully in operation, and the average completion time of public demands is reduced by 70%.

5. Trusted Execution Environment — the "safety lock" of the compliance base:

Strictly following the requirements of the Interim Measures for the Administration of Artificial Intelligence Anthropomorphic Interactive Services and the Regulation on Government Data Sharing, a security system with local data storage, hierarchical permission control and full life cycle audit is constructed. Adopting the sharing mechanism of "data available but invisible", hierarchical authorization management is implemented for sensitive data, so as to ensure that government AI agents can release productivity while keeping the bottom line of data security and citizen privacy.

III. From "Burden on Fingertips" to "Global Intelligent Governance": Business Model and Implementation Progress

The target users of the project cover five types of roles: district-level departments, sub-districts, communities, grid administrators and residents, forming a complete closed loop from governance decision-making to execution feedback.

In the scenario of community affairs handling, the platform uniformly receives work orders from multiple channels, including 12345 hotline transfer, automatic generation by AI visual recognition, residents' "snap and submit" reports, and tasks issued vertically by government departments, realizing full-link closed-loop management from work order discovery, intelligent identification, hierarchical distribution, collaborative disposal to archiving and feedback. In the scenario of public sentiment perception, through the multi-modal perception network of "drone cruise + AI vision + grid administrator visit + community patrol", the platform realizes zero-delay response of "hidden danger triggers early warning, early warning generates event". In the large-screen management scenario, the platform displays the real-time community operation situation in the form of heat maps, situation maps, GIS maps, etc., supporting "viewing the whole region with one screen". In the scenario of community governance insight assistant, grassroots staff can query community data and operation indicators through natural language, and the system automatically parses the intention and generates visualization results based on the ontology graph and NL2SQL engine.

In addition, the platform also provides an AI toolbox with functions such as conflict mediation assistance, special group care, community activity push, online discussion organization, community dynamic publicity, and multi-modal content generation, enabling community workers without technical background to call large models to complete complex data processing and content production tasks.

The practice in Futian District, Shenzhen has verified the feasibility of this path. In March 2026, Futian District officially released AI Intelligent Employee 2.0, introducing the first batch of autonomous AI agents, aiming to reduce the workload of front-line staff and improve the efficiency of community-level government services. The system has been upgraded from a Q&A assistant to an autonomous executor, with capabilities of task decomposition, process scheduling and autonomous decision-making, which can detect and correct execution errors and store operation experience for future use. At the e-Station Service Center in the Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone, AI agents can automatically download and review seven required documents, and issue an audit report within a few minutes, while the previous manual pre-review took one working day.

At present, the intellectualization of grassroots governance has changed from an "optional question" to a "must-answer question". When the balance of policy points to "actual effect" rather than "trace retention", and when the edge of technology is oriented to "empowerment" rather than "monitoring", the full-link intelligent governance platform with multi-agent collaboration as the architecture, ontology governance as the core and semantic intelligence as the bridge is turning "burden reduction" from a slogan into a mechanism, and turning "intelligent governance" from a vision into daily practice.