How Insurance Companies Deploy AI Support Agents Right

The biggest hurdle with AI agents in insurance isn't the model itself, it's guardrails and legacy integration.

Insurance rules change by state, and a hallucinated policy detail can turn into a massive legal headache fast. Most teams that get this right focus on three specific things:

1. Deterministic guardrails: The LLM shouldn't be allowed to guess policy terms. RAG setups need to pull strict, verified documentation and fail over gracefully to a human rep whenever confidence scores drop below a set threshold.
2. Legacy API wrappers: Insurance stack backends (like Guidewire or old mainframes) rarely have clean endpoints ready for modern AI tooling. Building secure middleware around those legacy systems usually takes up most of the dev time.
3. Strict audit trails: Every prompt, context retrieval, and decision path needs to be logged for compliance audits down the line.

If you're looking to build custom agents on your own cloud infrastructure rather than relying on off-the-shelf SaaS wrappers that fail compliance tests, check out Gaper.io (https://gaper.io/) to get specialized devs who build bespoke AI agent systems.

Curious what stack people here are using for agent guardrails lately? NeMo, Guardrails AI, or just custom middleware?