Conversation as Development: Rapidly Building an EHS Safety Management Platform with AI Agents

In the manufacturing sector, building an EHS (Environment, Health, and Safety) management system has always been a critical need for enterprises. However, traditional development approaches often face pain points such as long development cycles, high costs, and complex requirements communication. Now, with AI application generation Agents, a comprehensive safety management platform covering nine core modules can be rapidly generated simply through natural language descriptions and requirements document input. This article provides a detailed breakdown of this process, showing how the concept of "conversation as development" is truly being implemented in industrial safety production scenarios.

The Challenges Facing Traditional EHS System Development

Safety production management is a top priority for manufacturing enterprises, involving multiple complex business modules such as dual prevention mechanisms, special operations management, major hazard source monitoring, emergency response plans, and inspection management. EHS stands for Environment, Health, and Safety — a management framework that originated from the management practices of Western multinational corporations and has gradually become the standard management system for global manufacturing. In China, EHS management is closely tied to laws and regulations such as the Work Safety Law and the Law on Prevention and Control of Occupational Diseases. Enterprises not only need to establish comprehensive safety management systems but also need to achieve compliance documentation and data traceability through information technology. In recent years, the Ministry of Emergency Management has continued to promote the "Industrial Internet + Safety Production" action plan, requiring high-risk industries such as hazardous chemicals and mining to accelerate digital transformation, turning EHS systems from an "optional" to a "mandatory" investment.

Traditional EHS system development typically goes through lengthy processes including requirements research, prototype design, front-end and back-end development, testing, and deployment. Development cycles often stretch to several months, with customization costs ranging from hundreds of thousands to millions of yuan.

More critically, safety compliance logic is inherently extremely complex — data associations between work permits and gas analysis records, four-color risk classification and control of hazard points, and fine-grained configuration of organizational structures and role permissions — all of these require development teams to deeply understand industry standards, further raising the development threshold.

The Complete Workflow of AI Application Generation Agents

Project Initialization and Expert Mode Selection

Using Surprise Free's application generation Agent, the entire development process begins with creating a new application. First, name the project (e.g., "Safety Production Management Platform"), then complete the industry type and application type in sequence. A noteworthy detail: the platform provides an Expert Mode that can leverage deeper industrial experience knowledge, enabling the AI to better align with actual industry needs during the generation process.

AI application generation Agents represent a cutting-edge application of generative AI in software engineering. Their technology stack typically integrates the natural language understanding capabilities and code generation abilities of large language models (LLMs) with domain knowledge graphs. Unlike general-purpose code generation tools (such as GitHub Copilot), the goal of application generation Agents is not to assist programmers in writing code snippets, but to generate complete applications — including front-end interfaces, back-end logic, and database structures — end-to-end from requirements documents. This requires the AI to possess multi-layered reasoning capabilities: first, the requirements understanding layer, which transforms natural language descriptions into structured functional specifications; second, the architecture design layer, which selects appropriate technical architectures and data models based on business complexity; and finally, the code implementation layer, which generates runnable application code. The introduction of Expert Mode means the model incorporates domain-specific industry knowledge during training or inference, enabling it to understand industry terminology, compliance requirements, and best practices.

UI Style and Requirements Document Configuration

In the interface configuration step, users can choose from built-in UI styles on the platform or customize their own design. Requirements input is the core of the entire workflow — users simply submit their prepared requirements documents to the model. The platform also provides downloadable requirements templates to help users organize business requirements in a standardized format, reducing communication costs.

AI Automatically Parses Requirements and Builds the System

After the requirements document is submitted, the AI begins rapidly parsing the document content. This process goes far beyond simply "drawing interfaces" — the AI simultaneously accomplishes three things:

1. Interface Generation: Automatically builds front-end pages and interaction logic based on requirements
2. Business Logic Organization: Automatically understands complex safety compliance logic such as dual prevention mechanisms and work permit approvals
3. Database Construction: Designs and creates the underlying data structure in real-time, establishing data relationships

Analysis of the Nine Core Modules of the Generated Safety Production Management Platform

The final generated system encompasses nine core modules, essentially covering the standard business framework for safety production. From the left navigation bar, the system includes a complete EHS system foundation comprising dual prevention mechanisms, special operations management, major hazard source monitoring, and more.

Dual Prevention Mechanism: Four-Color Risk Classification and Control

In the dual prevention mechanism module, hazard points are managed with red, orange, yellow, and blue four-color classification and control. The dual prevention mechanism refers to the organic combination of two systems: "safety risk classification and control" and "hazard investigation and treatment," formally proposed by the State Council's Work Safety Committee in 2016 and promoted nationwide. Its core concept is to move safety management forward — first classifying and controlling risk sources through risk identification and assessment, then eliminating risks in their early stages through routine hazard investigations.

In the four-color classification standard, red represents major risks, orange represents significant risks, yellow represents general risks, and blue represents low risks. Each level corresponds to different control tiers and response frequencies. For example, red major risks typically require direct control by the enterprise's principal person in charge, while blue low risks can be managed at the team level for daily operations. This four-color risk classification management system fully complies with national safety production standards, with different risk levels corresponding to different control measures and response procedures at a glance. In terms of information technology implementation, it requires linking multiple data chains including risk databases, hazard registers, and rectification closure loops. The technical implementation complexity is high, yet the AI-generated system has automatically completed the construction of these data associations.

Special Operations Management: Automated Data Association

The highlight of the special operations management module is the automation of data associations. Special operations refer to non-routine work that may produce significant hazards during production operations. According to Safety Specifications for Special Operations in Chemical Production Units (GB 30871), special operations include eight categories: hot work, confined space entry, blind plate removal/installation, work at heights, lifting operations, temporary electrical work, excavation work, and road-breaking work. Each type of operation has strict approval processes and safety measure requirements. For example, gas detection must be performed before confined space entry, with oxygen levels maintained between 19.5% and 21%, and combustible gas concentration not exceeding 10% of the lower explosive limit.

The system automatically establishes data associations between work permits and gas analysis records. Key detection results such as oxygen levels and combustible gas concentrations can be clearly recorded, with support for automatic compliance determination. The work permit system is the core of special operations management — a single work permit needs to be linked with risk analysis, safety measure confirmation, gas detection records, approval signatures, and other steps. Any missing link could lead to compliance risks. This means approvers don't need to manually cross-check detection data; the system automatically determines whether all indicators meet safe operating conditions, significantly improving approval efficiency and compliance accuracy.

Major Hazard Source Monitoring

A major hazard source refers to a unit that produces, transports, uses, or stores hazardous materials on a long-term or temporary basis, where the quantity of hazardous materials equals or exceeds the critical threshold. According to the Interim Provisions on Supervision and Management of Major Hazard Sources of Hazardous Chemicals, enterprises must identify, assess, register, file, and establish real-time monitoring and early warning systems for major hazard sources. At the technical level, major hazard source monitoring typically requires connection to real-time data from industrial control systems such as DCS (Distributed Control System) and SCADA (Supervisory Control and Data Acquisition) for 24/7 online monitoring of key parameters including temperature, pressure, liquid level, and flow rate. When parameters exceed preset thresholds, the system must automatically trigger tiered alarms and initiate emergency response procedures. The major hazard source module in the AI-generated EHS system already includes pre-built functional frameworks for data acquisition, threshold management, alarm linkage, and historical data analysis. Enterprises can connect actual industrial sensors and control systems on this foundation.

Daily Inspections and Emergency Response Plan Management

At the daily execution level, the system covers the complete workflow from inspection task distribution and circulation to emergency drill records. Inspection personnel can carry out checks along system-assigned task routes, immediately reporting any issues found and triggering rectification processes. The emergency response plan module supports full-process management of drill planning, execution recording, and effectiveness evaluation.

Personnel Permissions and Organizational Structure Management

The system also builds comprehensive underlying support modules:

• External Personnel Management: Including contractor qualification review and visitor registration management
• Organizational Structure Configuration: Supporting flexible setup of multi-level organizational structures
• User Role Assignment: Fine-grained permission configuration for different roles such as safety officers and operators
• Operation Log Traceability: Complete audit records for all critical operations

How Far Is the AI-Generated EHS System from Delivery to Real-World Deployment?

Once the system is generated, it can be immediately put into trial operation in real scenarios — this is the most practically valuable feature of AI application generation Agents. Compared to traditional development approaches, this method compresses the development cycle from months to hours while significantly reducing dependence on professional development teams.

Of course, for an AI-generated system to truly be deployed in a production environment, it still needs to overcome several engineering hurdles. First is the data integration challenge — EHS systems typically need to interface with existing enterprise ERP, MES (Manufacturing Execution System), IoT platforms, and other systems, involving interface protocol adaptation and data format conversion. Second is performance and reliability — safety production systems are mission-critical business systems with high requirements for data consistency, system availability, and response speed. AI-generated code needs to undergo stress testing and security audits. Third is compliance review — safety production information systems in certain industries must pass classified protection evaluation, and the system's data storage, transmission encryption, and access control mechanisms all need to meet corresponding standards.

Therefore, AI-generated systems are best suited as a high-quality "starting point" — they rapidly build standardized business frameworks and data structures, upon which enterprises can fine-tune and optimize based on actual operational conditions rather than starting from scratch. This "rapid prototyping + iterative optimization" approach is precisely the most pragmatic value proposition of AI application generation Agents.

Conclusion: AI Agents Are Redefining Industrial Software Development

"Enabling everyone to become a developer of industrial applications" — this vision is gradually becoming reality through AI application generation Agents. When business personnel can directly describe requirements in natural language, and AI can understand industry standards, build compliance logic, and generate runnable systems, the threshold for software development is fundamentally redefined.

For fields with relatively high standardization like safety production management, the advantages of AI Agents are particularly evident — they come with built-in industry knowledge and compliance frameworks, generating EHS systems that are not only "functional" but also "compliant." As AI's understanding of industrial scenarios continues to deepen, such tools are expected to cover application development needs across more vertical industries in the future, gradually expanding from high-risk industries like chemicals and mining to broader manufacturing sectors including food processing and equipment manufacturing.