Earnix, the Boston-headquartered InsurTech provider specialising in pricing, underwriting and decisioning technology, has launched Agent Hub, bringing more than 25 insurance-specific AI agents and apps into its AI Orchestration System (AIOS).
The catalogue spans pricing, underwriting, modelling, customer engagement, data and technology, with 14 agents being demonstrated within Earnix solutions at its Excelerate London event.
Examples include Model Feature Mapper, which connects model features with the appropriate data variables to help pricing and actuarial teams work with trusted inputs while improving transparency and auditability around model-driven decisions.
Product Expert Advisor provides real-time answers to product questions using approved product information and expertise, while Premium Explainer generates personalised explanations of premiums based on individual policy information.
The use cases illustrate Earnix’s wider approach to agentic AI, with the company embedding agents within its pricing and rating, underwriting and customer engagement solutions rather than positioning them as standalone assistants.
Agent Hub can operate across insurers’ existing technology environments, including policy administration systems, data platforms, underwriting workbenches and customer portals. Earnix said agents work within defined permissions and traceability controls, with human oversight retained for decisions requiring judgement and accountability.
The platform brings together AI, data, models, business rules, workflows, governance and human expertise across Earnix’s pricing and rating, underwriting and customer engagement solutions.
Rather than positioning agents as separate tools, Earnix said AIOS enables them to operate within existing technology environments and apply relevant intelligence around consequential insurance decisions.
Earnix chief product and technology officer Be’eri Mart said, “Agentic AI becomes much more powerful when it can work with the data, models, and business context relevant to the task. The opportunity is not simply to automate a task, but to keep the information current as risk, customer behavior, and market conditions change. That is how insurers become more agile without losing control.”
The emphasis on business context is particularly relevant as insurers deal with changing risk profiles and market conditions. Earnix’s position is that agents need access to relevant and current information if they are to operate effectively within insurance workflows.
This also brings governance into sharper focus as AI moves closer to operational decision-making.
The agents in Agent Hub operate within defined permissions and traceability controls, while human oversight remains in place for decisions requiring judgement and accountability. For the individual use cases, that includes considerations around model transparency, approved product information and customer-facing explanations.
Datos Insights senior principal Meredith Barnes-Cook added, “Agentic AI is entering a more consequential phase for insurance. The question is no longer whether insurers can build agents, but whether they can deploy them into the decisions that affect growth, profitability, risk, and customer outcomes—within the guardrails that regulation and governance demand.”
She added, “That raises the bar considerably. Insurers will need to think as carefully about authority, accountability, and governance as they do about the intelligence itself. The companies that master that balance—where capability and control move together—will turn agentic AI into real business value.”
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