Vertical AI Agent Development Guide

With the continuous development and popularization of large models, many people have clearly realized that large model LLM has been very mature in simple office scenarios, but it is difficult to implement in some complex business scenarios. To achieve this goal, a lot of professional technical support is needed. As a result, a large number of AI Agents have been needed, but many people's understanding of Agents is still stuck in the past. In fact, the continuous development and improvement of the technology ecosystem has entered a new form . With the open source of DeepSeek-R1, many traditional companies have the opportunity to deploy large models themselves. After using high-performance AI at close range, many people's concepts have changed. In addition, with the MCP standard released by Anthropic in November 2024, the number of MCP servers has reached 4000+ in just 4 months, which has further cleared the obstacles to help improve AI performance. It even gave birth to general agents such as Manus.

AI agents are divided into two categories: vertical agents and general agents. Today we focus on the design and development of vertical agents.

1. Core Features and Classification of Vertical AI Agents

Vertical Agents are AI application systems that focus on specific scenarios. Compared with general agents, vertical agents are completely different.

Its core characteristics are first reflected in its specific target positioning. This type of agent is deeply optimized for a single scenario such as medical diagnosis or financial risk control, and its accuracy requirements are much higher than those of general-purpose agents. For example, medical diagnosis agents need to be able to accurately identify the clinical manifestations of specific diseases and provide diagnostic recommendations supported by evidence-based medicine, while financial risk control agents need to analyze transaction patterns in real time and identify potential fraud based on subtle abnormal indicators. This focus enables vertical agents to reach a judgment level close to that of professionals in specific fields.

Knowledge embedding is another core feature of vertical agents, which need to integrate all relevant knowledge bases in the scenario field. Taking the legal consulting agent as an example, it needs to integrate professional materials such as legal provisions, case analysis, and legal theories, and use RAG (retrieval enhanced generation) technology to improve the professionalism and accuracy of answers. This process not only involves the digitization of a large amount of domain knowledge, but also requires the establishment of an efficient semantic indexing system to ensure that the most relevant knowledge points can be quickly retrieved when users query. Financial investment advisory agents need to integrate market data, company financial reports, industry research reports and other materials to provide professional support for investment advice. In contrast, general agents can often only provide answers at the basic knowledge level and cannot meet the in-depth needs of professional scenarios.

General classification :

2. Analysis of the whole process of vertical agent development

1. Demand stage

• Business scenario combing : Demand analysis is the basic link in the development of vertical agents. The quality of this part determines the value realization of the final product. At this stage, it is necessary to conduct in-depth and detailed business scenario combing and use the 5W1H analysis method to capture key information. For example, in the medical imaging diagnosis scenario, we need to make it clear that the service objects of the Agent include both radiologists and clinicians, who have different professional backgrounds and usage requirements; the core tasks include the complete process from image data preprocessing, lesion detection to structured report generation; the trigger conditions include not only DICOM format image data upload, but also historical case review requests and other scenarios. This comprehensive demand analysis can ensure that the functional design of the Agent is seamlessly connected with the actual medical workflow.

• Value quantification model : The construction of a value quantification model is the key to proving the rationality of investment. We need to establish a multi-dimensional ROI calculation, not only considering direct costs, but also evaluating indirect benefits such as quality efficiency improvement and opportunity costs.

Taking the intelligent customer service scenario as an example, a mature Agent system can handle 300 standardized conversations per day, which is equivalent to replacing three human workers. Based on an annual salary of 150,000 yuan per person, the labor cost can be saved by 450,000 yuan per year. At the same time, the Agent's 24/7 service capability can shorten the average response time from 15 minutes to 30 seconds, improve customer satisfaction by about 27%, and indirectly increase customer retention by 600,000 yuan per year. Through such quantitative analysis, we can provide strong support for project decision-making and provide clear indicators for subsequent optimization directions.

2. Technical architecture design

The technical architecture design of vertical field agents needs to fully consider business characteristics and performance requirements. The typical layered architecture includes four core parts: perception layer, reasoning layer, execution layer and feedback learning layer.

• The perception layer is responsible for receiving and initially processing multimodal data. For example, in financial risk control scenarios, it is necessary to simultaneously process multi-source heterogeneous data such as transaction data streams, user behavior logs, and external credit scores.
• The reasoning layer, as the "brain" of the system, combines domain-adapted large models and knowledge graphs to achieve understanding and decision-making reasoning for complex scenarios. For example, in legal assistant applications, it is necessary to semantically associate the latest regulations with historical precedents to support similar case reasoning.
• The execution layer is responsible for converting decisions into actual actions and calling external systems through API orchestration. For example, in the smart manufacturing scenario, it needs to be seamlessly integrated with multiple enterprise systems such as MES and ERP to achieve automatic adjustment of production plans.
• The feedback learning layer continuously collects data from user interactions and business results, and continuously optimizes model performance through online learning algorithms.

The selection of key technologies requires balancing functional completeness, development efficiency, and maintenance costs.

• In terms of dialogue management, the LangChain framework provides a flexible agent building tool chain, which is suitable for rapid prototyping verification; while ModelScope-Agent has advantages in Chinese scenarios and tool calls, and is suitable for application development for domestic users.
• The memory mechanism is the key to ensuring the agent's coherent interactive experience. Using a vector database (such as Milvus or Pinecone) to store the conversation history and combining it with a decaying weight model can achieve context understanding for up to several hours, allowing the agent to maintain coherence in complex consulting scenarios.

In addition, in high-concurrency application scenarios, it is also necessary to consider introducing memory databases such as Redis as a cache layer for hot sessions to ensure millisecond-level response performance. The choice of technology stack should not only consider current needs, but also evaluate future scalability to reserve sufficient technical flexibility for business growth.

3. Data Engineering Implementation

Data engineering is a key link in optimizing the performance of vertical agent, and its core lies in building a high-quality professional knowledge base. The knowledge base construction process begins with comprehensive raw data collection, which requires comprehensive consideration of multiple data sources such as public literature, industry standards, and internal corporate information.

Taking the medical AI assistant as an example, its knowledge base should include multi-dimensional data such as medical textbooks, clinical guidelines, drug instructions, and anonymized typical cases. The collected data needs to be structured to convert unstructured text into standardized knowledge items, including concept extraction, relationship identification, and attribute labeling. The next data labeling link is very important. For structured data such as standard diagnosis and treatment specifications, experienced clinical experts are required to review and label; for unstructured data such as medical literature, a semi-automatic method combining crowdsourcing labeling and adversarial learning can be used. The algorithm first generates preliminary labels, and then manually checks and corrects errors to form high-quality labeling results. The professionally reviewed data will enter the vectorized storage stage, using an embedding model suitable for the characteristics of the domain (such as MedBERT and other medical field pre-training models) to generate semantic vectors, and build an efficient retrieval enhancement generation (RAG) index to achieve millisecond-level knowledge retrieval response.

The formulation of annotation specifications needs to fully combine industry standards and application scenario characteristics. In medical scenarios, disease diagnosis must follow the ICD-10 disease coding system to ensure compatibility with global medical information systems; drug annotation must adopt the ATC classification system to support automatic detection of drug interactions; medical procedures should be annotated according to the CPT coding specifications to facilitate docking with the medical insurance reimbursement system. In financial scenarios, financial data must comply with the XBRL eXtensible Business Reporting Language standard to support cross-institutional and cross-national financial data exchange and analysis; risk control indicators must follow the Basel III definition system to ensure the accuracy and consistency of risk assessment. A strict quality control mechanism must also be established during the annotation process, including multiple safeguards such as random sampling, cross-validation, and expert review to ensure that data quality meets industry application standards. High-quality annotated data not only improves the professional performance of the Agent, but also provides a reliable foundation for subsequent model fine-tuning.

3. Typical Industry Application Models

1. Medical field

• Design pattern : multimodal fusion (text + image + sensor data)

Artificial intelligence applications in the medical field are achieving unprecedented diagnostic accuracy through multimodal fusion technology. This design pattern integrates text, medical images and various sensor data to provide all-round support for clinical decision-making. Taking the chest CT image analysis agent as an example, the system cleverly integrates three core components: the deep learning model based on ResNet-50 is responsible for image recognition. After training on more than 100,000 chest CT images, the model can identify 17 common lesions including lung nodules, emphysema and interstitial lung disease with an accuracy of 92.7%; the BioBERT model is pre-trained specifically for medical corpus and can generate structured reports that conform to the language habits of radiologists, greatly reducing the time for report writing; at the same time, the system seamlessly connects with the hospital's existing information system through the FHIR standard interface to achieve real-time synchronization of electronic medical records and ensure the smooth transmission of diagnostic information in the medical workflow. In clinical trials at tertiary hospitals, the system shortened the doctor's imaging diagnosis time from an average of 15 minutes to 4 minutes, while increasing the detection rate of early lung cancer by about 18%.

2. Education

Educational technology is reshaping the traditional teaching model with the help of AI technology. In the lesson preparation stage, the new generation of lesson preparation assistants has significantly improved teachers' work efficiency by integrating advanced models such as Stable Diffusion and GPT-4. Specifically, teachers only need to input the course theme and key concepts, and the system can automatically generate a complete lesson plan outline containing teaching objectives, key and difficult points analysis, and teaching activity design. At the same time, the Stable Diffusion model automatically generates teaching illustrations that meet age characteristics based on the course content. These illustrations are optimized based on the principles of educational psychology and can effectively improve students' knowledge absorption rate. In the evaluation stage, the intelligent evaluation system based on the Transformer architecture has completely changed the traditional way of grading essays. The system analyzes multi-dimensional indicators such as semantic coherence, argument logic, and vocabulary diversity to achieve automatic grading that is highly consistent with manual grading, with an error rate of less than 3%. Application data from a key middle school in Beijing shows that after teachers use the system, the time for grading is reduced by 78%, and students significantly increase their writing enthusiasm and ability improvement speed due to immediate feedback.

3. Industrial Manufacturing

AI applications in the field of industrial manufacturing are leading the intelligent manufacturing revolution with their excellent real-time performance and reliability. To meet the demanding needs of modern factories, engineers have developed an efficient edge computing deployment solution that is particularly suitable for equipment predictive maintenance scenarios. The solution uses the Rust language to implement the core logic, ensuring memory safety while providing performance close to that of the C language. The system achieves millisecond-level data acquisition through a distributed sensor network, covering multi-dimensional parameters such as temperature, vibration, sound, and current, and then inputs the data into a quantitatively optimized ONNX format model for anomaly detection reasoning. When the system detects signs of potential failure, it immediately triggers the maintenance API to achieve intelligent intervention on the equipment. The end-to-end response time of the entire process is controlled within 50 milliseconds. At the same time, the system architecture supports production line-level concurrent processing capabilities and can monitor the operating status of more than 1,000 devices at the same time. On the production line of an automotive parts manufacturer, one year after the system was deployed, the equipment's unexpected downtime was reduced by 43%, maintenance costs were reduced by 28%, and the first-time pass rate of product quality was increased by 7.5%, fully demonstrating the actual value of AI in industrial scenarios.

IV. Key Challenges and Breakthrough Paths

1. The small sample learning dilemma

In the actual implementation process, AI applications often face the challenge of data scarcity, which is particularly evident in professional fields and vertical industries. The cost of obtaining a large amount of labeled data is very high. In response to this problem, the industry has developed a series of solutions. Contrastive learning technology performs particularly well in scenarios where labeled data is extremely limited. By building similarity relationships between samples to learn feature representations, the model accuracy can still be improved by 15-20% even when there are less than 100 labeled data. The introduction of frameworks such as SimCLR and MoCo enables the model to learn meaningful feature representations from unlabeled data, greatly reducing the dependence on labeled data. In the scenario of model migration between devices, meta-learning methods such as MAML (Model-Agnostic Meta-Learning) enable the model to quickly adapt to the new environment through the strategy of "learning how to learn", and the convergence speed is increased by about 3 times, significantly shortening the model deployment cycle. For compliance-sensitive fields such as finance and healthcare, synthetic data augmentation technology provides a feasible path to circumvent data privacy restrictions. By generating diverse synthetic data, the diversity of the data set is increased by 40%, effectively preventing the phenomenon of model overfitting.

Solution :

Case : In the practice of the power industry, these technologies have achieved remarkable results. Take a provincial power grid company as an example. They faced the problem of insufficient rare fault samples in the power equipment fault detection project, especially for the specific fault type of high-voltage transformers, of which there were only a dozen cases in historical records. The engineering team cleverly applied the GAN-based data synthesis technology to generate hundreds of physically reasonable simulated fault thermal images by learning the feature distribution of a limited number of real fault infrared thermal images. These synthetic data are not only highly consistent with the real data in visual features, but also accurately simulate the changing laws of thermal distribution under different load conditions. After the actual deployment, the fault detection model enhanced by these synthetic data successfully identified two potential transformer failures in advance, avoiding the possible economic losses of millions of yuan.

2. Multimodal alignment problem

With the increasing complexity of AI application scenarios, single-modal information processing can no longer meet actual needs, and multimodal fusion has become a key path to improve system performance. However, the heterogeneity and time inconsistency between different modal data have brought severe alignment challenges. The industry has formed a relatively clear technical route to address this problem, mainly from the two dimensions of hierarchical fusion and attention mechanism. In terms of hierarchical fusion, studies have shown that the progressive strategy from early fusion to late fusion can balance computational complexity and fusion effect. Early fusion retains the integrity of the original information by directly splicing at the pixel or feature level, but the computational overhead is large; while late fusion integrates the prediction results of each modality through weighted voting or ensemble learning methods at the decision layer, which is more computationally efficient but may lose complementary information between modalities. In practice, the multi-level fusion architecture can usually achieve the best balance, that is, preliminary fusion at the intermediate feature layer and then fine integration at the decision layer.

In terms of attention mechanism, the cross-modal Transformer architecture achieves dynamic alignment between different modalities through self-attention and cross-attention mechanisms, especially in multimodal data processing with complex spatiotemporal relationships such as speech-text-video. This technology can automatically learn the correspondence between different modalities without manually designing complex alignment rules. In a smart city security project, researchers applied this technology to the abnormal behavior detection system, processing three modal data: surveillance video, environmental audio, and historical text records. Through a carefully designed cross-modal attention network, the system can capture subtle abnormal patterns that are difficult to identify with a single modality, such as normal walking accompanied by abnormal sounds in the video. Actual evaluation shows that the multimodal fusion method significantly improves the F1-score of abnormal behavior detection from 0.72 of a single modality to 0.89, reduces the false alarm rate by nearly 60%, greatly reduces the workload of security personnel, and improves system reliability. This successful case fully demonstrates the great potential of multimodal fusion technology in complex scenarios.

V. Deployment and Optimization Strategy

1. Robustness Verification System

When AI systems move from the laboratory to the production environment, robustness verification becomes a key link to ensure the stability and reliability of the system. In the field of financial risk control, stress testing is particularly important due to the high concurrency characteristics of the business and strict real-time requirements. Take the risk control agent of an Internet financial company as an example:

# Stress test script framework (taking financial risk control agent as an example)
locust -f stress_test.py \
  --users 1000 \           # Simulate concurrent users
  --spawn-rate 10 \        # Number of new users per second
  --host https://api.risk-control.com \
  --csv=report             # Output performance report

This stress test solution verifies that the system's response time is controlled within 150ms under 1,000-level concurrency, ensuring the stability of the system under extreme conditions. In addition to basic performance testing, security verification is equally important. Engineers built an adversarial sample generation framework based on the FGSM algorithm to test the model's resistance to malicious input. The model enhanced by adversarial training reduced the attack success rate by about 65%, significantly improving system security.

In order to deal with the degradation of model performance caused by changes in data distribution over time, the team designed a real-time monitoring mechanism based on KL divergence, which automatically triggers model hot updates when the distribution difference exceeds the threshold. The following figure shows the concept drift detected by a payment platform during the holidays:

2. Continuous learning mechanism

Continuous optimization of AI systems after deployment is the key to maintaining competitiveness, especially in data-sensitive industries. Federated learning effectively solves the contradiction between privacy protection and model iteration by using a "model-to-data" approach rather than a "data-to-model" approach. Its core implementation logic is as follows:

class  FederatedAgent  {
    public  void  train (Model globalModel)  {
        List<ClientData> clients = getEdgeNodes();   // Get edge nodes
        for (ClientData client : clients) {
            Model localModel = downloadModel(globalModel);
            localModel.train(client.data);           // Local training
            uploadGradients(localModel);             // Gradient upload
        }
        aggregateGradients();                        // Global aggregation
    }
}

The advantage of this architecture is that the data is always kept locally, and only the model parameters are transmitted in the network, which greatly reduces the risk of data leakage. In medical industry applications, a tertiary hospital and multiple medical institutions in the region built a lung nodule detection system that adopted this architecture, which steadily improved the model AUC by 0.5-0.8% per week, and accumulated an increase of about 8.5% after three months. The following figure shows the performance improvement curve of federated learning in medical scenarios:

The federated learning architecture not only protects patient privacy, but also fully utilizes the value of data dispersed across institutions, significantly exceeding the performance ceiling of traditional centralized learning methods. This continuous learning mechanism provides an effective way for AI systems to maintain their competitiveness in practical applications.

Based on the above information, the development of vertical AI Agents will be able to break through the transformation bottleneck of "laboratory-production line". In the technology ecosystem of 2025, it is recommended to give priority to fields with clear ROI calculation scenarios such as medical care, education, and intelligent manufacturing, while paying attention to the integration and innovation of knowledge engineering and reinforcement learning.