A vertical AI agent is an autonomous intelligent system that operates within a specific market or niche. By only using verified data, workflows and standards from a single domain, it is claimed that vertical agents can be more reliable and capable than general purpose AI models.
How do vertical AI agents work?
Vertical AI agents tend to run off structured domain knowledge and a highly optimised large language model (LLM). The knowledge structure of the domain is usually provided by ontologies and knowledge graphs. These aim to ensure high levels of accuracy and explainability (XAI). Interpreting natural language input, and providing fluent and context-aware answers, is the job of the LLM.
When the model’s constraints permit, a vertical LLM can enable the agent to reason using unstructured information. Hybrid RAG can also be deployed to provide intelligent systems with better memory recall and contextual understanding in a way that non-technical users are more likely to understand, get value from, and interact with.
A 2025 paper published in the Journal of Medical Internet Research explored the development of an ontology-driven multi-agent system in healthcare for diabetes management.
What is a typical vertical AI workflow?
Typically, an AI agent will receive input via queries, workflow triggers, or sensor signals from the domain environment. It will recall relevant historical information or prior interactions to provide continuity. It will then analyse the input, map out potential paths, and apply domain rules as defined by the ontology. This is the reasoning part of the system.
The agent may also run tasks like risk analysis, image classification, or compliance checking using specialised models. If the agent is integrated with other tools, it might query external databases, retrieve documents, or monitor Internet of Things (IoT) devices. These are part of the wider cognitive capabilities of vertical AI.
The next stage of the workflow is when the agent generates outputs or actions. These might be delivering reports, performing an automated task, or escalating an action for human review. Using a feedback loop, a vertical AI agent can also learn from results and refine subsequent actions, thereby improving its service over time.
Is a vertical AI agent worth it?
Implementing a general purpose ‘horizontal’ AI agent is usually faster and less costly than a specialised vertical agent. However, domains with complex rules, and low tolerance for errors, are more likely to require a vertical agent for their precision and actionable insights.
The case for building, implementing and maintaining a domain intelligent system with a vertical AI agent is often strong in regulated niches with heavy data usage, such as medicine and advanced manufacturing. In these sectors, most decisions need to be accompanied by detailed explanations (XAI), including data lineage, which vertical AI is better placed to deliver.
Errors can be extremely costly in regulated industries, and collaboration often takes place across different disciplines and teams. For these reasons, such organisations are more willing to invest in structuring their knowledge, including controlled vocabulary, which is the foundation of vertical AI.
In other domains, however, scale is often the deciding factor for choosing a vertical AI agent over a simpler LLM. In other words, vertical AI agents need to be designed in a way that makes them re-usable and interoperable with other systems. By scaling up and creating a network effect, a vertical AI agent can deliver greater operational efficiency gains and therefore a better return on investment.
What challenges does vertical AI face?
Several challenges need to be overcome before vertical AI agents can fulfil their promise of expert-level accuracy, be it diagnosing a patient, detecting fraud, or generating legal documentation.
On a technical level, organisations often lack clean, structured, and compliant data assets. Making data machine-readable usually requires domain experts to reach time-consuming consensus on the design of an ontology (although modern ontology-as-a-service platforms can make this process easier).
Organisations may need custom APIs or middleware to connect legacy software with agents. In most regulated areas, human oversight is required by law, which limits the scope of automation. Cultural factors can also impede the adoption of automated systems, requiring an organisational reset.