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What are Vertical AI Agents? – Everything You Need to Know


We are entering the era of AI agents, which can make autonomous decisions without much human oversight. Along with the rise of AI agents is the birth of various new concepts, typically including vertical AI agents. This concept is considered to quickly thrive and replace incumbent, traditional SAAS platforms for digital transformation and improved operational efficiency. So, what are vertical AI agents exactly? How are they adopted in real-world business use cases? Which impact are they having on industries worldwide? Keep reading the article and let Designveloper help you answer these questions!

What are Vertical AI Agents?

Vertical AI agents are intelligent systems optimized to perform domain-specific functions or tasks instead of implementing different workflows horizontally. That trait makes them different from traditional SAAS (Software-as-a-Service) platforms and general-purpose AI models. 

Definition of vertical AI agents

In other words, traditional SAAS software is widely adopted across sectors and departments, like customer service, healthcare, or finance management. But vertical AI agents only possess specialized skills (e.g., healthcare-related) and therefore focus on specific domains. 

These agents have the following core components:

  1. Domain-Specific LLMs (Large Language Models): These fine-tuned models are trained on domain-specialized data to provide agents with reasoning capabilities. This helps them understand jargon, contexts, and regulations related to their field. 
  1. Memory: This module helps vertical AI agents continuously be conscious of context and historical interactions. This allows them to offer personalized responses over time. 
  1. Cognitive Skills: Apart from the core LLM, AI agents are also integrated with smaller specialist models or algorithms to perform highly structured tasks and handle real-time processing. For example, you have a vertical AI agent processing finance management, powered by finance-specialized LLMs that power the agent. The agent is also equipped with smaller specialist models to acquire cognitive skills, like anomaly detection, financial statement proxying, or task calculation. 
  1. Business Logic and Workflow Engine: Vertical AI agents don’t depend on open-ended prompts, but use built-in business logic and workflow engines to execute structured tasks more reliably.
  1. Tool Integration: Vertical AI agents will connect with core business systems (e.g., ERPs, CRMs, or EHRs) in real-time through RAG-based retrieval or APIs. This allows the agents to exchange and update data or trigger workflows instantly.

How do vertical AI agents work?

The way vertical AI agents work is similar to how general-purpose agents operate. The only difference lies in its narrow scope of work. Vertical AI agents focus on industry-specific tasks, while their general-purpose counterparts tend to process different tasks across sectors and might lack domain-specialized depth. 

Combining the mentioned core components, vertical AI agents act as digital employees, instead of rigid, programmed systems that only answer questions. Let’s take a detailed look at how they work in reality:

  • The agents receive inputs (which can be customer questions, sensor signals, or workflow triggers) from their surroundings. 
  • Using the memory module, they remember relevant past interactions and data to stay consistent and offer personalized experiences. 
  • The core LLM behind the agents uses its reasoning engine to analyze the inputs, adopt industry-specific rules and workflows, and choose the right action. 
  • The agents continue to activate specialized models and use external tools through APIs to perform the action. For instance, to monitor credit risks, the financial AI agent will leverage both a financial analysis model and tools (e.g., a scoring engine) to discover and flag credit default risks. 
  • Once the agents process everything, they’ll start executing the action autonomously. 
  • They can learn from results and adjust their subsequent actions dynamically. For instance, if a flagged fraud case was false, the financial AI agent would refine their sensitivity. 

Vertical AI agents work in a feedback loop. This makes them more intelligent over time, especially in evolving environments. 

Why do vertical AI agents thrive? 

Traditional SaaS platforms are cloud-based solutions you can access online, often through web browsers. For a long time, they’ve been considered foundational solutions to digitize and streamline various business functions and workflows, from HR and finance to customer service. SaaS platforms often scale horizontally and prioritize general applications instead of focusing on deep domain expertise. This trait is potentially double-edged.

Why do vertical AI agents thrive? 

According to Y Combinator’s experts, various big companies don’t look into B2B SaaS. One visible reason is that they want some solutions that focus on specialized skills and care deeply about obscure issues. Further, the booming of AI is the main attribute to this change. Vertical SaaS solutions have long existed, but their potential has been considered “uncertain.” That’s why horizontal SaaS has always preceded its vertical counterparts in these years. 

However, the introduction of large language models (LLMs) and advanced AI capabilities has changed the game. In other words, AI has empowered vertical applications. To say it exactly, vertical AI strengthens specialized workflows – whether healthcare, finance, or legal – in a way legacy vertical and horizontal SaaS couldn’t do. 

  • Automate language-based, high-volume vertical tasks. Legacy SaaS software struggles to handle high-cost, repetitive workflows, like financial reporting, marketing content generation, or claims review. But AI has the ability to analyze vast amounts of data, reason through complex contexts, and perform those tasks intelligently. The integration of AI has reduced manual efforts and accelerated workflows while increasing efficiency. 
  • Offer predictions, personalization, and flexibility. Generic SaaS platforms work based on predefined rules and are hardly customized for your unique workflows. Vertical AI, meanwhile, can adapt to evolving changes (i.e., in internal policies or customer demands) and forecast dynamic variables based on historical and real-time data. 

Growth trend of vertical AI agents: Complement or replace legacy SaaS?

With the mentioned capabilities and benefits, it’s no wonder that vertical AI agents are expected to increase significantly in their market value. Particularly, their revenue will reach $16.1 billion in 2025, which continues to grow annually at 24.5% as an inevitable result of the increasing demand to handle industry-specific tasks with greater accuracy and efficiency. 

Bessemer Venture Partners predicted that the market share of vertical AI agents will be at least tenfold larger than that of legacy vertical SaaS. In comparison, vertical AI can earn around 80% of the yearly contract value of these SaaS systems. Vertical AI startups also grow extremely fast, at an impressive 400% year-over-year, and earn a gross margin of roughly 65%. 

However, it doesn’t mean vertical AI agents will completely replace traditional SaaS software. Bessemer Venture Partners found that most vertical AI players are now introducing new features that don’t directly impact or compete with traditional SaaS. In other words, vertical AI agents basically do what legacy SaaS software hasn’t done and add new value to the old solution.   

5 Real-World Use Cases of Vertical AI Agents

To help you better understand how vertical AI agents work in practice, let’s take a look at some real-world examples as follows:

5 Real-World Use Cases of Vertical AI Agents

Healthcare

Vertical AI agents are integrated into electronic health records (EHRs) to support diagnosis and reduce administrative burden. In particular, they can:

  • Improve diagnosis by detecting abnormal patterns in medical images or cross-checking symptoms with medical literature
  • Implement patient-facing tasks (e.g., answering symptom questions, tracking medication schedules, or booking appointments)
  • Support clinical documentation and medical billing
  • Automate insurance paperwork (e.g., claims review or prior authorization submission)
  • Support patients with mental illness through emotional conversations.

Example: SmarterDX offers two clinical AI agents to boost care. SmarterPrebill analyzes patient charts to identify incorrect or missing diagnoses, improving care quality. Meanwhile, its SmarterDenials can quickly identify the root cause of denied claims and automatically create data-backed clinical appeal letters.

Vertical AI agents are helping legal professionals streamline their routine tasks:

  • Answering questions by sifting through relevant statutes, case law, court rulings, or legal precedents.
  • Drafting and reviewing contracts.
  • Checking compliance and identifying risky clauses or inconsistent parts within contracts.
  • Helping clients understand simple legal forms, legal procedures, and file complaints.

Example: CoCounsel is a professional-grade AI legal agent that supports end-to-end workflows. In particular, it answers your legal research questions with evidence backed by trusted Westlaw and Practical Law resources. Further, it helps with document drafting and analysis.  

Finance

Vertical AI agents are also customized to perform specialized tasks across the BFSI (Banking, Financial Services, and Insurance) industry. Here is what they can help you with finance: 

  • Automatically analyze a company’s performance and financial health to unlock revenue opportunities and identify potential risks.
  • Automate accounting tasks, like bookkeeping or classifying transactions.
  • Identify risky or fraudulent transactions and monitor regulatory compliance.
  • Evaluate loan applications, score credit, and accelerate approvals.
  • Support financial planning and investment.
  • Answer finance-related questions (e.g., about loan terms or account charges).

Example: Vic.ai is a smart accounting agent. It autonomously executes invoice processing tasks to minimize errors and accelerate the month-end close. Additionally, it supports your company in spend management, PO matching, compliance tracking, payment analysis, and risk prediction.  

Automotive

Vertical AI agents are making vehicles more autonomous and enhancing driver experiences in different ways: 

  • Interact with drivers using natural language processing and context awareness capabilities.
  • Auto-adjust in-vehicle settings (e.g., seat position, media, or climate control) to fit individual preferences.
  • Analyze driver behavior through in-car sensors and cameras and send alerts when abnormal signals arise.
  • Optimize routes using real-time traffic, weather, road closures, and personal preferences. 
  • Prevent vehicle breakdown by continuously monitoring environmental data (e.g., battery levels or engine temperature).

Example: Mercedes-Benz started using Google’s Automotive AI Agent to strengthen its MBUX Virtual Assistant. With this new agentic AI capability, the intelligent in-vehicle assistant enables drivers to search and navigate more easily through natural language conversations. Further, the Automotive AI Agent helps the MBUX process complex, multi-turn conversations and remember past interactions. This integration offers personal driving experiences and reduces the risk of accidents. 

Manufacturing

Vertical AI agents help manufacturers automate and optimize several aspects of their industrial operations inside manufacturing plants. With these agents, manufacturers can: 

  • Conduct predictive maintenance seamlessly to monitor machines and their potential failures.
  • Analyze and adjust production lines in real-time to reduce delays or bottlenecks.
  • Manage supply chains, from demand forecasting and inventory management to risk detection.
  • Automatically identify defects and control quality
  • Simulate new product designs, production plans, or factory layouts. 
  • Continuously monitor regulatory and safety compliance to reduce accidents and avoid regulatory penalties.

Example: Neurala’s Vision AI software automatically inspects and controls quality. The software helps manufacturers identify defects and anomalies of various products, whether electronic components, kitted parts, or surfaces. 

Challenges and Expert Advice for Using Vertical AI Agents

Challenges and Expert Advice for Using Vertical AI Agents

While vertical AI agents have wide applications across departments and sectors, they still come with several challenges. If you decide to adopt vertical AI agents into your business workflows, you have to consider the following challenges to ensure effective use. 

Data quality and compliance issues

Vertical AI agents use industry-specific data to perform specialized tasks or workflows. However, this data, especially related to legal services, finance, and healthcare, is highly sensitive and regulated. So, many companies struggle to protect this data and ensure compliance with domain-specific regulations.

Solution: When adopting vertical AI agents, your company must adopt robust security measures to protect this data in transit and at rest. Further, choose AI agents with built-in compliance features and implement frequent audits to maintain transparency in AI-generated actions. This ensures the ethical and responsible use of the agents, while keeping the data always ready for them to use and analyze. 

Integration with legacy systems

Many companies still depend on legacy infrastructure. But integrating vertical AI agents into these systems is not always easy due to data silos, incompatibility, or lack of standardized APIs.

Solution: To address this problem, your company should modernize core parts of your business systems (e.g., establishing data lakes or middleware layers to centralize data) and then use APIs to connect them with vertical AI agents. Don’t rush to adopt the agents for critical workflows. Instead, pilot them with isolated, unimportant tasks first to check their performance and compatibility. Besides, you should consider working with suppliers offering vertical-specific integrations.

Change management resistance

Many employees resist adopting vertical AI agents because of their fear of job loss or struggle with new AI-based workflows.

Solution: Successful adoption of vertical AI agents in your business is not just about technical, but also about cultural. So, to encourage AI use among your staff for their routine tasks, your company should involve staff early and show them how AI will support, not replace, them. Further, you should provide role-specific training and upskill your employees to use AI agents more effectively.

Conclusion

Vertical AI agents, once again, are intelligent systems optimized to perform industry-specific workflows or tasks. They are emerging as a new trend, and we predict they’ll soon become a new norm in the future. This stems from the fact that more and more businesses worldwide want intelligent digital workforces to support their existing teams in automating specialized tasks to achieve the best results at work. However, apart from benefits, we also see vertical AI agents present several challenges you should consider to use them more effectively. 

If you want a trusted, experienced partner to help you overcome these problems, Designveloper is here to help. We have deep technical expertise and hands-on experience in AI development and integration into core business systems across industries. Further, we can harness the latest AI technologies to boost your operational efficiency and employee productivity while ensuring work quality. Contact us to discuss your idea further and start your AI journey from now!

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