The future of enterprise AI is not a single assistant. It is about specialized AI agents working together to solve complex business problems.
At Data + AI Summit (DAIS) 2026, Databricks introduced Genie Ontology, which helps AI agents understand business concepts and relationships instead of relying only on keywords. For life insurance customer support, this means AI agents can better understand policy types, riders, claim status, eligibility rules, and coverage conditions.
Life insurance customer support teams answer questions about policies, premiums, coverage, and claims. They often need to search multiple systems and lengthy policy documents, which takes time and can lead to inconsistent responses.
How It Works
At Hoonartek, we built a multi-agent customer support solution to solve this, without replacing any of the systems already in place. We used Databricks Agent Bricks, which gives you a framework for building and deploying specialized agents directly on top of the data and systems already in Databricks, so we did not need to stitch together a custom agent pipeline or stand-up separate infrastructure. Agent Bricks also plugs directly into Unity Catalog and MLflow, so governance and monitoring came built in from day one, not bolted on afterward. That is what let us move from idea to a production-ready system fairly quickly.
A Multi-Agent Supervisor coordinates two specialized agents:
- Sub-Agent A retrieves policy overviews, coverage, benefits, exclusions, and terms from policy documents.
- Sub-Agent B validates customer profiles, premium history, policy status, and claims eligibility from enterprise systems.
When a customer asks, “Am I eligible for the accidental death benefit, and are my premiums up to date?”, the Supervisor routes each part of the request to the right agent and combines the results into one clear response.
Enterprise-Grade by Design
Built on the Databricks Data Intelligence Platform with Agent Bricks, the solution includes:
- Natural language interactions for support teams, where representatives can ask questions the way they would ask a colleague, without learning a special query format.
- LLM-based evaluation and SME feedback for continuous improvement, where subject matter experts regularly review and score the agent’s answers, and that feedback is used to keep improving accuracy over time.
- MLflow lineage, monitoring, and quality control for production readiness, where every answer can be traced back to how it was generated, keeping the system auditable and reliable enough to run in production.
Business Impact
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Faster customer responses:
Questions that used to take several minutes to answer now get resolved in seconds, since the agents pull everything together automatically.
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Reduced manual effort:
Support staff no longer must manually search through policy documents or check multiple systems for every query, so they have more time for harder cases.
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Consistent and accurate answers:
Every customer gets the same quality of response, no matter which rep handles the query, since the answers all come from the same agents and data.
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Enterprise-ready governance and monitoring:
Every interaction is tracked and can be reviewed, so the business always knows what the AI is doing and can trust it for real production use.
Looking Ahead
As our solution evolves, we plan to leverage Genie Ontology to strengthen the Supervisor Agent by providing richer business context. This will help the agents route customer requests more accurately, deliver more consistent responses, and provide better recommendations while reducing manual effort for support teams.
By combining Databricks Agent Bricks, structured and unstructured data, and Genie Ontology, organizations can build a smarter and more reliable customer support experience for life insurance.
If you’re exploring Multi-Agent AI for customer support or other enterprise workflows, I’d be happy to connect and share our experience.
