What are vertical AI agents?

Sometimes called domain‑specific agents or function-focused agents, vertical AI agents are artificial intelligence systems that are fine-tuned for use in a specific industry. They are useful in industries such as healthcare or banking. Vertical AI agents are also a good fit for a specific business function, such as customer service or supply chain management.

While most general-purpose AI is built to handle a wide range of tasks across many fields, or a high volume of general queries, vertical agents are different. These agents use targeted data and specialized expertise to solve problems in a more precise way.

Like most agents, they help streamline workflows, automate repetitive tasks and support informed decision-making. But their real value comes from incorporating industry rules, compliance information or particular industry terminology or jargon that general models lack.

By combining advanced AI technologies with domain-specific knowledge, vertical AI agents cater to the particular needs of specific industries and offer greater precision and specialization.

Vertical AI agents differ from general-purpose AI in their focus and functions. While general-purpose AI systems, such as OpenAI’s ChatGPT and other generative pre-trained transformer (GPT) models, handle a wide range of tasks, vertical AI agents are developed for specific industries or functions. This specialization allows them to deliver greater accuracy and relevance for their targeted use cases.

Recent advancements in large language models (LLMs), open source frameworks and cloud technologies have accelerated the evolution of vertical AI agents. These developments have created a system of providers (including larger AI companies and emerging AI startups) that are making vertical AI agents more accessible and scalable. This approach allows for increasing adoption across a range of industries.

From diagnosing medical conditions in healthcare to optimizing inventory in retail, vertical AI agents are purpose-built to address complex, domain-specific challenges that require a high degree of specialization. They can be applied at different scales—from doing specific tasks that can be automated in a simple way, to more complex end-to-end workflows that require greater coordination and sophistication. Some ways in which they are used include: 

Equipped with deep domain knowledge, vertical AI agents offer data-driven insights and recommendations. This feature makes them invaluable for decision-making in complex or high-stakes environments. By analyzing large datasets and identifying patterns, they help organizations address highly specialized problems, especially in complex fields where domain expertise is critical.

Example: In manufacturing, vertical AI agents analyze production data to optimize supply chains and predict equipment failures. By identifying inefficiencies and anticipating potential issues, these agents help manufacturers improve operational efficiency and reduce downtime. Similarly, in agriculture, vertical AI agents monitor crop health by using satellite imagery and suggest interventions to maximize yield and minimize waste.

Organizations use vertical AI agents to automate repetitive tasks or time-consuming work within specific industries. These agents handle administrative tasks that would otherwise require significant human effort, reducing human error and freeing up workers to focus on higher-value activities. Automation powered by vertical AI agents helps streamline workflows and improve efficiency.

Example: In healthcare, vertical AI agents assist with medical coding, treatment summaries and appointment scheduling. By automating these administrative tasks, healthcare providers can allocate more time to patient care while ensuring accuracy and compliance. In finance, vertical AI agents automate compliance monitoring and risk assessment, helping organizations adhere to regulations while reducing the time and resources required for manual oversight.

Vertical AI agents provide tailored experiences to customers by analyzing user behavior and preferences within a specific context. They streamline conversations, connect users to the right support and sometimes take actions (such as issuing refunds or updating account settings) on behalf of the user. These agents are often embedded within live chat tools or customer relationship management (CRM) systems like Salesforce to support customer satisfaction and loyalty.

Example: IBM’s watsonx Assistant powers virtual agents that integrate with backend systems to answer routine questions, retrieve account details and initiate transactions. These assistants help customer service teams reduce call volume and resolution time while improving customer satisfaction. For personalizing customer experiences, an e-commerce business might use vertical AI agents to suggest products based on browsing and purchase history, creating a customized shopping experience.

Vertical AI agents can be used to support research and development in specialized fields such as pharmaceuticals or engineering. By handling complex computations and data analysis, vertical AI agents enable breakthroughs that were previously impossible or prohibitively time-consuming.

Example: IBM’s digital health research team developed a multimodal platform called Health Guardian that integrates data from mobile apps, wearables and sensors to generate predictive models for patient care.

In research settings, this agent has been used to assess patient mobility and mental health status, demonstrating how domain-specific AI can provide nuanced insights in real-world care contexts. In pharmaceuticals, vertical AI agents analyze vast datasets to identify potential drug candidates, accelerating the development of new treatments.

In dynamic environments, vertical AI agents can monitor issues such as network performance and suggest adjustments as needed. These agents often use streaming data pipelines and feedback loops to adapt their actions based on live inputs. Their ability to process and act on data in real time makes them critical for industries such as transportation or IT security, where timing is everything.

Example: In industrial operations, vertical AI agents monitor equipment performance by using data from Internet of Things (IoT) devices and maintenance logs. They predict potential component failures and optimize inventory flow, ensuring smoother operations and minimizing disruptions. In IT security, vertical AI agents might detect and respond to threats in real time, supporting system reliability and preventing breaches.

In banking, insurance or other highly regulated industries, vertical AI agents can help organizations stay compliant with complex rules and regulations. This approach can reduce legal risks and save organizations time and resources.

Example: In finance, vertical AI agents monitor transactions for suspicious activity, ensuring compliance with anti-money laundering (AML) regulations. They also assist with underwriting and fraud detection by analyzing credit reports and transaction histories. Fintech companies sometimes use these agents to automatically assess loan applications and maintain audit trails for compliance.

With training on specific programming languages or frameworks, vertical AI agents help developers write code, generate documentation and spot bugs or inconsistencies. They can also orchestrate complex testing workflows for faster, more consistent quality control.

Example: Software development teams use vertical AI agents to automatically update or rewrite code in real time, improving development speed and reducing errors. These agents also streamline testing workflows, ensuring consistent quality in software releases and enabling faster deployment cycles.

Vertical AI agents in the legal field require a high degree of accuracy, explainability and alignment with legal standards. They assist with tasks such as document review, contract analysis and regulatory research, helping legal teams save time and reduce costs.

Example: Some law firms use AI-powered legal agents to draft documents and analyze case law. These tools, built on foundation models fine-tuned with legal knowledge, operate behind secure firewalls to help ensure data privacy and compliance.

Vertical AI agents collect data, analyze it using specialized algorithms, and extract insights to make recommendations or perform tasks. As agentic tools, they perform multi-step tasks, make decisions and interact with application programming interfaces (APIs) or other agents.

Developers build most vertical AI agents on top of general-purpose foundation models, such as OpenAI’s GPT or open source models. They then fine-tune, instruction-tune or adapt them with retrieval-augmented generation (RAG) to specialize in a specific domain. The agents often improve over time by learning from new data and feedback, allowing them to adapt to changing conditions or evolving industry requirements.

The main components that underpin these agents include:

Vertical AI agents rely on large, high-quality datasets that are directly relevant to the industry or task they are designed for. Organizations draw in data from various sources, such as historical records, real-time inputs or external databases. Increasingly, vertical AI agents are multimodal, which means they can understand and combine different types of text, images, sensor data and voice inputs.

The quality and relevance of the curated data determine the accuracy and effectiveness of the agent. For example, in healthcare, the data must be comprehensive (in ways such as covering diverse patient demographics) to ensure reliable diagnoses.

Before being used, the data undergoes preprocessing steps such as cleaning, normalization and feature extraction to ensure that it is usable by the AI model. And in regulated fields, the data must also meet strict privacy and security standards, such as HIPAA or GDPR compliance.

Vertical AI agents use algorithms tailored to the needs of their domain. For instance, a legal AI agent might use natural language processing (NLP) to analyze contracts, while an agricultural AI agent might employ computer vision to monitor crop health through satellite imagery. With advanced techniques and specialized domain knowledge, the agent can better understand the context in which it operates.

The machine learning (ML) models at the core of vertical AI agents are trained on domain-specific data to develop expertise in their field. These models learn patterns, relationships and rules from the data to perform their tasks effectively.

They use supervised learning (in which training data is labeled), unsupervised learning (in which the model finds patterns on its own), or reinforcement learning, which is useful for tasks that involve long-term decision-making, such as optimizing supply chains.

Organizations integrate vertical AI agents with existing software, hardware or workflows within their target industry. For example, a manufacturing AI agent might connect to Internet of Things (IoT) sensors on factory equipment, or a retail AI agent might integrate with e-commerce platforms.

Connection to existing tools like customer relationship management systems (CRMs) electronic health records (EHRs), scheduling systems, supply chain systems or financial databases. These connections are typically made through secure APIs, which allow the vertical AI agent to read, write and interact with data in a controlled way.

Rather than simply answering questions, many vertical agents are designed to perform multi-step workflows—often autonomously. This use of agentic AI requires a set of coordinated components:

• Planners break down complex goals into smaller tasks.
• Executors carry out individual steps, often invoking external tools or systems.
• Memory modules track progress and context over time.
• Retrievers access relevant information as needed from databases or document stores.

Together, these components allow agents to operate in structured, goal-oriented ways for complex problem-solving and tasks. To support multi‑step complex tasks, agents can retain memory of past interactions to help them maintain context over time.

Although vertical AI agents operate with significant autonomy, they still require human-in-the-loop (HITL) oversight, especially in high-stakes or highly regulated fields. This means human operators can intervene when the agent encounters uncertainty or sensitive issues.

In addition to human involvement, agents that handle sensitive tasks might also operate under governance or compliance frameworks. These details include things like audit logs, access controls and explainability tools that help users understand why the agent acted in a certain way. Feedback mechanisms built into vertical AI agents enable them to learn from their environment, which allows for adaptability and helps them improve over time.

Vertical AI agents deliver highly specialized solutions tailored to specific industries. Their deep domain expertise ensures accuracy and relevance, making them useful for tasks like medical diagnoses, fraud detection or supply chain optimization.

By automating repetitive tasks and reducing human error, they improve efficiency and streamline workflows—which in turn can help in developing a competitive advantage. Beyond efficiency, vertical AI agents are well suited to developing personalized experiences and can speed innovation and development in complex, data-driven fields.

Vertical AI agents do come with some challenges. While their narrow focus is a strength, it also makes them less versatile than general-purpose AI systems. One key issue is their reliance on large, high-quality, domain-specific datasets, which can be difficult to obtain and standardize. Also, their development and training can be resource-intensive—and often requires ongoing maintenance and updates to remain relevant.

This maintenance can impact the pricing and business models of providers, which range from broad enterprise contracts to usage-based Software-as-a-Service (SaaS) subscriptions. And as with any AI technology, issues such as data privacy, regulatory compliance and ethical concerns must be carefully managed.