How Vertical AI Agents Are Transforming Insurance Claims Workflows

Vertical AI agents are redefining how insurers manage complex, multi-step workflows by overcoming the limitations of traditional robotic process automation (RPA). These domain-specific agents use orchestrator-worker models, advanced retrieval methods like GraphRAG, and knowledge graphs to support dynamic decision-making in scenarios such as long-term care (LTC) claims and auto insurance assessments. Their adaptability makes them particularly valuable as insurers face increasing demands for personalized service, regulatory compliance, and real-time responsiveness.

In long-term care claims, AI agents process heterogeneous data from wearables and provider notes, assess policy eligibility through knowledge graph queries, and synthesize decisions transparently for adjusters. Similarly, auto claims benefit from these agents’ ability to interpret data from telematics and ADAS systems, helping identify risk factors and potential fraud.

The core value lies in agentic AI’s ability to manage open-ended problems through orchestration: an orchestrator delegates tasks to specialized agents (workers), who process inputs like mobility data, trip signals, and unstructured notes. The use of knowledge graphs and provenance-supported retrieval ensures that the AI’s reasoning is grounded, explainable, and auditable—enhancing both efficiency and trust.

As insurance organizations look to the future, scaling responsibly means identifying the right use cases for agentic AI, investing in domain ontologies, integrating environmental data, and continually measuring impact. When properly implemented, vertical AI agents are a powerful solution for managing the complexity and scale of modern insurance workflows.